Project Part 2

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This notebook contains the code samples found in Chapter 3, Section 5 of Deep Learning with R. Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.

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Data Exploration & Preparation

• Our goal in the second part of the assignment is to predict how good a (new) customer will pay back their credit card depts. In the data set application data from current customers (the first 18 attributes) together with their status (last attribute; target) are given.
  
• The attributes from the applications are

Attribute Name	Explanation	Remarks
ID	Client	number
CODE_GENDER	Gender
FLAG_OWN_CAR	Is there a car
FLAG_OWN_REALTY	Is there a property
CNT_CHILDREN	Number of children
AMT_INCOME_TOTAL	Annual income
NAME_INCOME_TYPE	Income category
NAME_EDUCATION_TYPE	Education level
NAME_FAMILY_STATUS	Marital status
NAME_HOUSING_TYPE	Way of living
DAYS_BIRTH	Birthday	Count backwards from current day (0), -1 means yesterday
DAYS_EMPLOYED	Start date of employment	Count backwards from current day(0). If positive, it means the person unemployed.
FLAG_MOBIL	Is there a mobile phone
FLAG_WORK_PHONE	Is there a work phone
FLAG_PHONE	Is there a phone
FLAG_EMAIL	Is there an email
OCCUPATION_TYPE	Occupation
CNT_FAM_MEMBERS	Family size

• The last attribute status contains the “pay-back behavior”, i.e. when did that customer pay back their depts:
  • 0: 1-29 days past due
  • 1: 30-59 days past due
  • 2: 60-89 days overdue
  • 3: 90-119 days overdue
  • 4: 120-149 days overdue
  • 5: Overdue or bad debts, write-offs for more than 150 days
  • C: paid off that month
  • X: No loan for the month Please note: We are learning only the pay-back behavior. The decision, i.e. if we accept a customer or not, is done in another process step – not here!

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Main task

• Design your network. Why did you use a feed-forward network, or a convolutional or recursive network – and why not?
  
• Use k-fold validation (with k = 10) to find the best hyperparameters for your network.
• Use the average of the accuracy to evaluate the performance of your trained network.
• Find a “reasonable” good model. Argue why that model is reasonable. If you are not able to find a reasonable good model, explain what you all did to find a good model and argue why you think that’s not a good model.
  
• Save your trained neural network with save_model_hdf5. Also save your data sets you used for training, testing and validation.

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Some hints

• Data preprocessing is easier here; no feature engineering is needed.
• You may be able to reuse parts of the exercises we used in our examples during lectures.
• All in- and output values need to be floating numbers (or integers in exceptions) in the range of [0,1].
• Please note that a neural network expects a R matrix or vector, not data frames. Transform your data (e.g. a data frame) into a matrix with data.matrix if needed.
  
• There are some models which show an accuracy higher than 90% (!) for training (and test) data – after learning more than 1000 epochs.

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Important notes

• Single-label, Multiclass classification problem on page 73 in Deep Learning with R
• Spaces must be removed in between ‘{r}' and '’, else an error with ‘’ will be produced
• K-Fold Validation on page 83ff and 94ff in Deep Learning with R
• Page 110, use Last-Layer activation softmax and loss function categorical_crossentropy
• Convolutional network ausgeschlossen, weil hauptsächlich Pattern recognition/image classification
• Recursive ausgeschlossen, weil hauptsächlich für TimeSeries-Vorhersagen verwendet, oder für Vorhersagen
• Feed-Forward, weil Classification-Task

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Data import

#install.packages("tidymodels")
#install.packages("themis")
library(here)
library(tidyverse)
library(ggplot2)
library(dplyr)
library(tensorflow)
library(tfdatasets)
library(tidymodels)
library(keras)
library(caret)
library(themis)
#LOAD DATA
setwd(getwd())
dataIn = "../Data/Dataset-part-2.csv"
data_in <- read.csv(dataIn,header = TRUE, sep =',')
#View(data_in)
data <- data.frame(data_in)
summary(data)

       ID          CODE_GENDER        FLAG_OWN_CAR       FLAG_OWN_REALTY     CNT_CHILDREN     AMT_INCOME_TOTAL 
 Min.   :5008804   Length:67614       Length:67614       Length:67614       Min.   : 0.0000   Min.   :  26100  
 1st Qu.:5465941   Class :character   Class :character   Class :character   1st Qu.: 0.0000   1st Qu.: 112500  
 Median :5954270   Mode  :character   Mode  :character   Mode  :character   Median : 0.0000   Median : 157500  
 Mean   :5908133                                                            Mean   : 0.4206   Mean   : 178867  
 3rd Qu.:6289080                                                            3rd Qu.: 1.0000   3rd Qu.: 225000  
 Max.   :7965248                                                            Max.   :19.0000   Max.   :6750000  
 NAME_INCOME_TYPE   NAME_EDUCATION_TYPE NAME_FAMILY_STATUS NAME_HOUSING_TYPE    DAYS_BIRTH     DAYS_EMPLOYED   
 Length:67614       Length:67614        Length:67614       Length:67614       Min.   :-25201   Min.   :-17531  
 Class :character   Class :character    Class :character   Class :character   1st Qu.:-19438   1st Qu.: -2886  
 Mode  :character   Mode  :character    Mode  :character   Mode  :character   Median :-15592   Median : -1305  
                                                                              Mean   :-15914   Mean   : 62022  
                                                                              3rd Qu.:-12347   3rd Qu.:  -321  
                                                                              Max.   : -7489   Max.   :365243  
   FLAG_MOBIL FLAG_WORK_PHONE    FLAG_PHONE       FLAG_EMAIL     OCCUPATION_TYPE    CNT_FAM_MEMBERS 
 Min.   :1    Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Length:67614       Min.   : 1.000  
 1st Qu.:1    1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:0.0000   Class :character   1st Qu.: 2.000  
 Median :1    Median :0.0000   Median :0.0000   Median :0.0000   Mode  :character   Median : 2.000  
 Mean   :1    Mean   :0.2028   Mean   :0.2742   Mean   :0.1005                      Mean   : 2.174  
 3rd Qu.:1    3rd Qu.:0.0000   3rd Qu.:1.0000   3rd Qu.:0.0000                      3rd Qu.: 3.000  
 Max.   :1    Max.   :1.0000   Max.   :1.0000   Max.   :1.0000                      Max.   :20.000  
    status         
 Length:67614      
 Class :character  
 Mode  :character  
                   
                   
                   

plot(data$status)

##Cleanup

# Check for duplicates 
sum(duplicated(data))

[1] 0

# No duplicates

#Remove ID (irrelevant) and FLAG_MOBIL (always 1)
data <- data %>% select(-ID, -FLAG_MOBIL)
cols <- c("CODE_GENDER","FLAG_OWN_CAR","FLAG_OWN_REALTY","NAME_INCOME_TYPE","NAME_EDUCATION_TYPE", "NAME_FAMILY_STATUS", "NAME_HOUSING_TYPE","FLAG_WORK_PHONE","FLAG_PHONE","FLAG_EMAIL", "OCCUPATION_TYPE","status")
cols

 [1] "CODE_GENDER"         "FLAG_OWN_CAR"        "FLAG_OWN_REALTY"     "NAME_INCOME_TYPE"   
 [5] "NAME_EDUCATION_TYPE" "NAME_FAMILY_STATUS"  "NAME_HOUSING_TYPE"   "FLAG_WORK_PHONE"    
 [9] "FLAG_PHONE"          "FLAG_EMAIL"          "OCCUPATION_TYPE"     "status"             

data[cols] <- lapply(data[cols],factor)

# Replacing empty values with "Unknown"
levels(data$OCCUPATION_TYPE) <- c(levels(data$OCCUPATION_TYPE), "Unknown")
data$OCCUPATION_TYPE[is.na(data$OCCUPATION_TYPE)] <- "Unknown"

# Replacing C and X in Status
levels(data$status)[levels(data$status)=="C"] <- "6"
#data$status[data$status == "X"] <- 7
levels(data$status)[levels(data$status)=="X"] <- "7"
# #Convert factors into numericals
# data %<>% mutate_if(is.factor, as.numeric)

summary(data)

 CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN     AMT_INCOME_TOTAL              NAME_INCOME_TYPE
 F:43924     N:43107      N:21090         Min.   : 0.0000   Min.   :  26100   Commercial associate:15640  
 M:23690     Y:24507      Y:46524         1st Qu.: 0.0000   1st Qu.: 112500   Pensioner           :11982  
                                          Median : 0.0000   Median : 157500   State servant       : 5044  
                                          Mean   : 0.4206   Mean   : 178867   Student             :    4  
                                          3rd Qu.: 1.0000   3rd Qu.: 225000   Working             :34944  
                                          Max.   :19.0000   Max.   :6750000                               
                                                                                                          
                    NAME_EDUCATION_TYPE            NAME_FAMILY_STATUS           NAME_HOUSING_TYPE
 Academic degree              :   38    Civil marriage      : 6016    Co-op apartment    :  227  
 Higher education             :16890    Married             :44906    House / apartment  :60307  
 Incomplete higher            : 2306    Separated           : 4125    Municipal apartment: 2303  
 Lower secondary              :  716    Single / not married: 9528    Office apartment   :  587  
 Secondary / secondary special:47664    Widow               : 3039    Rented apartment   : 1020  
                                                                      With parents       : 3170  
                                                                                                 
   DAYS_BIRTH     DAYS_EMPLOYED    FLAG_WORK_PHONE FLAG_PHONE FLAG_EMAIL    OCCUPATION_TYPE  CNT_FAM_MEMBERS 
 Min.   :-25201   Min.   :-17531   0:53904         0:49071    0:60819    Unknown    :20699   Min.   : 1.000  
 1st Qu.:-19438   1st Qu.: -2886   1:13710         1:18543    1: 6795    Laborers   :12425   1st Qu.: 2.000  
 Median :-15592   Median : -1305                                         Sales staff: 6899   Median : 2.000  
 Mean   :-15914   Mean   : 62022                                         Core staff : 6059   Mean   : 2.174  
 3rd Qu.:-12347   3rd Qu.:  -321                                         Managers   : 4950   3rd Qu.: 3.000  
 Max.   : -7489   Max.   :365243                                         Drivers    : 4427   Max.   :20.000  
                                                                         (Other)    :12155                   
     status     
 0      :52133  
 1      : 6491  
 7      : 5790  
 6      : 1805  
 2      :  712  
 5      :  374  
 (Other):  309  

Preprocessing

set.seed(1)
trainIndex <- initial_split(data, prop = 0.8, strata = status) 
trainingSet <- training(trainIndex)
testSet <- testing(trainIndex)
status_folds <- vfold_cv(trainingSet, v = 10, strata = status)
status_folds

#  10-fold cross-validation using stratification 

# Remove outliers (Out of 1.5x Interquartile Range) only on training set
# CNT_CHILDREN
boxplot(trainingSet$CNT_CHILDREN, horizontal=TRUE, main="CNT_CHILDREN")

Q1_Child <- quantile(trainingSet$CNT_CHILDREN, .25)
Q3_Child <- quantile(trainingSet$CNT_CHILDREN, .75)
IQR_Child <- IQR(trainingSet$CNT_CHILDREN)
# Now we keep the values within 1.5*IQR of Q1 and Q3
trainingSet <- subset(trainingSet, trainingSet$CNT_CHILDREN > (Q1_Child - 1.5*IQR_Child) & trainingSet$CNT_CHILDREN < (Q3_Child + 1.5*IQR_Child))
dim(trainingSet)

[1] 53330    17

# AMT_INCOME_TOTAL
boxplot(trainingSet$AMT_INCOME_TOTAL, horizontal=TRUE, main="AMT_INCOME_TOTAL")

Q1_AIT <- quantile(trainingSet$AMT_INCOME_TOTAL, .25)
Q3_AIT <- quantile(trainingSet$AMT_INCOME_TOTAL, .75)
IQR_AIT <- IQR(trainingSet$AMT_INCOME_TOTAL)
# Now we keep the values within 1.5*IQR of Q1 and Q3
trainingSet <- subset(trainingSet, trainingSet$AMT_INCOME_TOTAL > (Q1_AIT - 1.5*IQR_AIT) & trainingSet$AMT_INCOME_TOTAL < (Q3_AIT + 1.5*IQR_AIT))
dim(trainingSet)

[1] 51748    17

set.seed(5)
preprocRecipe <-
  recipe(status ~., data = data) %>%
  step_dummy(all_nominal(), -status,  one_hot = TRUE) %>%
  step_range(all_predictors(), -all_nominal(), min = 0, max = 1)%>%
  step_smote(status, over_ratio = 1) %>%
 # step_downsample(status, under_ratio = 1, skip=TRUE) %>%
 # step_smote(status, over_ratio = 1, skip=TRUE) %>%
 # step_smotenc(status, over_ratio = 1) %>%
 # step_adasyn(status, over_ratio = 1) %>%
 # step_nearmiss(status, over_ratio = 1) %>%
   
  step_dummy(status,  one_hot = TRUE)# %>%

In this step the above defined receipt is extracted using the prep() function, and then use the bake() function to transform a set of data based on that recipe.

# retain = TRUE and new_data = NULL ensures that pre-processed trainingSet is returned 
trainingSet_processed <- preprocRecipe %>%
  prep(trainingSet, retain = TRUE) %>%
  bake(new_data = NULL)
testSet_processed <- preprocRecipe %>%
  prep(testSet) %>%
  bake(new_data =testSet)

#summary(trainingSet_processed)

Check data

# sum(trainingSet_processed$status_X0 == 1)
# sum(trainingSet_processed$status_X1 == 1)
# sum(trainingSet_processed$status_X2 == 1)
# sum(trainingSet_processed$status_X3 == 1)
# sum(trainingSet_processed$status_X4 == 1)
# sum(trainingSet_processed$status_X5 == 1)
# sum(trainingSet_processed$status_X6 == 1)
# sum(trainingSet_processed$status_X7 == 1)

# Turn data frame into data matrix
matrix_data <- trainingSet_processed %>% select(-tail(names(trainingSet_processed), 8))
matrix_targets <- trainingSet_processed %>% select(tail(names(trainingSet_processed), 8))

matrix_data_test  <- testSet_processed %>% select(-tail(names(testSet_processed), 8))
matrix_targets_test  <- testSet_processed %>% select(tail(names(testSet_processed), 8))

# summarize the class distribution
percentage <- 100-prop.table(table(data$status)) * 100

#class_counts <- table(data$status)
class_counts <- matrix_targets %>%
  summarize_all(funs(sum(. == 1)))
majority_class_count <- max(class_counts)
relative_class_counts <-  majority_class_count /class_counts

cbind(freq=table(data$status), percentage=percentage)

   freq percentage
0 52133   22.89615
1  6491   90.39992
2   712   98.94696
3   195   99.71160
4   114   99.83140
5   374   99.44686
6  1805   97.33043
7  5790   91.43668

#Subset only 100 entries for testing
#matrix_data <- matrix_data[1:100, ]
#matrix_targets <- matrix_targets[1:100, ]

Build Model

#train_data <- matrix_data
train_data <- data.matrix(matrix_data)
test_data <- data.matrix(matrix_data_test)
train_targets <- data.matrix(matrix_targets)
test_targets <- data.matrix(matrix_targets_test)



# Function to build the model
build_model <- function() {
  model <- keras_model_sequential() %>%
    #layer_batch_normalization(axis = -1L, input_shape = dim(train_data)[[2]]) %>%
    layer_dense(units = 128, activation = "relu", input_shape = dim(train_data)[[2]]) %>%
    layer_dense(units = 128, activation = "relu") %>%
    layer_dense(units = 128, activation = "relu") %>%
    layer_dense(units = 128, activation = "relu") %>%
    layer_dense(units = 128, activation = "relu") %>%
    #layer_dropout(0.3) %>%
    layer_dense(units = 8, activation = "softmax") 

  model %>% compile(
    optimizer = optimizer_sgd(learning_rate = 0.1),
    # optimizer = optimizer_adam(learning_rate = 0.1),
    loss = "categorical_crossentropy",
    metrics = "categorical_accuracy"
  )

}

#Yannick
#install.packages("kerasR")
# library(kerasR)
# model <- keras_model_sequential()
# model %>%
#          layer_dense(units = 64, activation = 'relu', dim(train_data)[[2]]) %>%
#          layer_dropout(rate = 0.2) %>%
#          # layer_dense(units = 30, activation = 'relu') %>%
#          # layer_dropout(rate = 0.3) %>%
#          layer_dense(units = 20, activation = 'relu') %>%
#          layer_dropout(rate = 0.2) %>%
#          layer_dense(units = 8, activation = 'softmax')
# summary(model)
# model %>%
#          compile(loss = 'categorical_crossentropy',
#                  optimizer = 'adam',
#                  metrics = 'accuracy')
# history <- model %>%
#          fit(train_data,
#              train_targets,
#              epochs = 1500,
#              batch_size = 1024,
#              validation_split = 0.2,
#              verbose =2,
#              class_weight = list(relative_class_counts))
# plot(history)
# model %>%
#          evaluate(test_data, test_targets)
# pred <- model %>% predict(test_data, batch_size = 32)
# y_pred = round(pred)
# # Confusion matrix
# library(caret)
# confusion_matrix <- caret::confusionMatrix(matrix(pred), matrix(test_targets))
# length(test_targets)
# table(Predicted = round(pred), Actual = test_targets)

K-Fold-Validation

k <- 2
indices <- sample(1:nrow(train_data))
folds <- cut(indices, breaks = k, labels = FALSE)

num_epochs <- 1000
all_acc_histories <- NULL
for (i in 1:k) {
  cat("processing fold #", i, "\n")

  val_indices <- which(folds == i, arr.ind = TRUE)
  val_data <- train_data[val_indices,] #test_data#
  val_targets <- train_targets[val_indices,] #test_targets#

  partial_train_data <- train_data[-val_indices,]
  partial_train_targets <- train_targets[-val_indices,]
  model <- build_model()

  # Train the model (in silent mode, verbose=0)
  # Batch size https://stats.stackexchange.com/questions/153531/what-is-batch-size-in-neural-network
  # One epoch = one forward pass and one backward pass of all the training examples
  # Batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you'll need.
  # Number of iterations = number of passes, each pass using [batch size] number of examples. To be clear, one pass = one forward pass + one backward pass (we do not count the forward pass and backward pass as two different passes).
  # Batch size 32 much faster than 1, also the smaller the batch the less accurate the estimate of the gradient will be.
  history <- model %>% fit(
    partial_train_data, partial_train_targets,
    validation_data = list(val_data, val_targets),
    epochs = num_epochs, batch_size = 8192, verbose = 2#, class_weights = percentage
  )
  acc_history <- history$metrics$val_categorical_accuracy
  all_acc_histories <- rbind(all_acc_histories, acc_history)
}

processing fold # 1 
Epoch 1/1000

WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0338s vs `on_train_batch_end` time: 0.0619s). Check your callbacks.

20/20 - 4s - loss: 2.0739 - categorical_accuracy: 0.1475 - val_loss: 2.0633 - val_categorical_accuracy: 0.1785 - 4s/epoch - 175ms/step
Epoch 2/1000
20/20 - 0s - loss: 2.0555 - categorical_accuracy: 0.2180 - val_loss: 2.0459 - val_categorical_accuracy: 0.2596 - 327ms/epoch - 16ms/step
Epoch 3/1000
20/20 - 0s - loss: 2.0352 - categorical_accuracy: 0.2748 - val_loss: 2.0209 - val_categorical_accuracy: 0.2845 - 350ms/epoch - 18ms/step
Epoch 4/1000
20/20 - 0s - loss: 2.0046 - categorical_accuracy: 0.2932 - val_loss: 1.9821 - val_categorical_accuracy: 0.3027 - 319ms/epoch - 16ms/step
Epoch 5/1000
20/20 - 0s - loss: 1.9577 - categorical_accuracy: 0.3074 - val_loss: 1.9253 - val_categorical_accuracy: 0.3131 - 307ms/epoch - 15ms/step
Epoch 6/1000
20/20 - 0s - loss: 1.8933 - categorical_accuracy: 0.3178 - val_loss: 1.8531 - val_categorical_accuracy: 0.3453 - 334ms/epoch - 17ms/step
Epoch 7/1000
20/20 - 0s - loss: 1.8370 - categorical_accuracy: 0.3308 - val_loss: 1.8198 - val_categorical_accuracy: 0.3364 - 344ms/epoch - 17ms/step
Epoch 8/1000
20/20 - 0s - loss: 1.7883 - categorical_accuracy: 0.3343 - val_loss: 1.7476 - val_categorical_accuracy: 0.3625 - 346ms/epoch - 17ms/step
Epoch 9/1000
20/20 - 0s - loss: 1.7377 - categorical_accuracy: 0.3519 - val_loss: 1.7115 - val_categorical_accuracy: 0.3520 - 365ms/epoch - 18ms/step
Epoch 10/1000
20/20 - 0s - loss: 1.6867 - categorical_accuracy: 0.3732 - val_loss: 1.6895 - val_categorical_accuracy: 0.3701 - 345ms/epoch - 17ms/step
Epoch 11/1000
20/20 - 0s - loss: 1.6402 - categorical_accuracy: 0.3880 - val_loss: 1.6000 - val_categorical_accuracy: 0.4192 - 351ms/epoch - 18ms/step
Epoch 12/1000
20/20 - 0s - loss: 1.5939 - categorical_accuracy: 0.4072 - val_loss: 1.5688 - val_categorical_accuracy: 0.4111 - 352ms/epoch - 18ms/step
Epoch 13/1000
20/20 - 0s - loss: 1.5601 - categorical_accuracy: 0.4187 - val_loss: 1.4994 - val_categorical_accuracy: 0.4318 - 352ms/epoch - 18ms/step
Epoch 14/1000
20/20 - 0s - loss: 1.5275 - categorical_accuracy: 0.4276 - val_loss: 1.4613 - val_categorical_accuracy: 0.4627 - 337ms/epoch - 17ms/step
Epoch 15/1000
20/20 - 0s - loss: 1.4785 - categorical_accuracy: 0.4471 - val_loss: 1.4087 - val_categorical_accuracy: 0.4778 - 348ms/epoch - 17ms/step
Epoch 16/1000
20/20 - 0s - loss: 1.4487 - categorical_accuracy: 0.4616 - val_loss: 1.3859 - val_categorical_accuracy: 0.4786 - 336ms/epoch - 17ms/step
Epoch 17/1000
20/20 - 0s - loss: 1.4060 - categorical_accuracy: 0.4750 - val_loss: 1.3327 - val_categorical_accuracy: 0.5037 - 351ms/epoch - 18ms/step
Epoch 18/1000
20/20 - 0s - loss: 1.3534 - categorical_accuracy: 0.4882 - val_loss: 1.3544 - val_categorical_accuracy: 0.4915 - 352ms/epoch - 18ms/step
Epoch 19/1000
20/20 - 0s - loss: 1.3657 - categorical_accuracy: 0.4859 - val_loss: 1.3444 - val_categorical_accuracy: 0.5034 - 338ms/epoch - 17ms/step
Epoch 20/1000
20/20 - 0s - loss: 1.3556 - categorical_accuracy: 0.5012 - val_loss: 1.3019 - val_categorical_accuracy: 0.5054 - 328ms/epoch - 16ms/step
Epoch 21/1000
20/20 - 0s - loss: 1.2994 - categorical_accuracy: 0.5085 - val_loss: 1.2711 - val_categorical_accuracy: 0.5277 - 352ms/epoch - 18ms/step
Epoch 22/1000
20/20 - 0s - loss: 1.3238 - categorical_accuracy: 0.5139 - val_loss: 1.3265 - val_categorical_accuracy: 0.4916 - 322ms/epoch - 16ms/step
Epoch 23/1000
20/20 - 0s - loss: 1.2615 - categorical_accuracy: 0.5225 - val_loss: 1.2426 - val_categorical_accuracy: 0.5274 - 332ms/epoch - 17ms/step
Epoch 24/1000
20/20 - 0s - loss: 1.2636 - categorical_accuracy: 0.5298 - val_loss: 1.5064 - val_categorical_accuracy: 0.4499 - 332ms/epoch - 17ms/step
Epoch 25/1000
20/20 - 0s - loss: 1.2354 - categorical_accuracy: 0.5374 - val_loss: 1.1916 - val_categorical_accuracy: 0.5662 - 360ms/epoch - 18ms/step
Epoch 26/1000
20/20 - 0s - loss: 1.2094 - categorical_accuracy: 0.5413 - val_loss: 1.1884 - val_categorical_accuracy: 0.5485 - 346ms/epoch - 17ms/step
Epoch 27/1000
20/20 - 0s - loss: 1.1894 - categorical_accuracy: 0.5508 - val_loss: 1.2014 - val_categorical_accuracy: 0.5431 - 346ms/epoch - 17ms/step
Epoch 28/1000
20/20 - 0s - loss: 1.4883 - categorical_accuracy: 0.4862 - val_loss: 1.4050 - val_categorical_accuracy: 0.5390 - 346ms/epoch - 17ms/step
Epoch 29/1000
20/20 - 0s - loss: 1.1741 - categorical_accuracy: 0.5888 - val_loss: 1.0833 - val_categorical_accuracy: 0.6073 - 365ms/epoch - 18ms/step
Epoch 30/1000
20/20 - 0s - loss: 1.1678 - categorical_accuracy: 0.5653 - val_loss: 1.1075 - val_categorical_accuracy: 0.6017 - 368ms/epoch - 18ms/step
Epoch 31/1000
20/20 - 0s - loss: 1.1886 - categorical_accuracy: 0.5640 - val_loss: 1.1616 - val_categorical_accuracy: 0.5941 - 343ms/epoch - 17ms/step
Epoch 32/1000
20/20 - 0s - loss: 1.1283 - categorical_accuracy: 0.5782 - val_loss: 1.1235 - val_categorical_accuracy: 0.5850 - 348ms/epoch - 17ms/step
Epoch 33/1000
20/20 - 0s - loss: 1.0969 - categorical_accuracy: 0.5913 - val_loss: 1.0996 - val_categorical_accuracy: 0.5800 - 343ms/epoch - 17ms/step
Epoch 34/1000
20/20 - 0s - loss: 1.0822 - categorical_accuracy: 0.5978 - val_loss: 1.2018 - val_categorical_accuracy: 0.5305 - 350ms/epoch - 18ms/step
Epoch 35/1000
20/20 - 0s - loss: 1.0556 - categorical_accuracy: 0.6014 - val_loss: 1.0133 - val_categorical_accuracy: 0.6247 - 363ms/epoch - 18ms/step
Epoch 36/1000
20/20 - 0s - loss: 1.0764 - categorical_accuracy: 0.5978 - val_loss: 1.1969 - val_categorical_accuracy: 0.5580 - 349ms/epoch - 17ms/step
Epoch 37/1000
20/20 - 0s - loss: 1.1187 - categorical_accuracy: 0.5972 - val_loss: 1.1288 - val_categorical_accuracy: 0.5675 - 368ms/epoch - 18ms/step
Epoch 38/1000
20/20 - 0s - loss: 1.0231 - categorical_accuracy: 0.6120 - val_loss: 1.0873 - val_categorical_accuracy: 0.5885 - 366ms/epoch - 18ms/step
Epoch 39/1000
20/20 - 0s - loss: 1.0305 - categorical_accuracy: 0.6151 - val_loss: 0.9490 - val_categorical_accuracy: 0.6449 - 361ms/epoch - 18ms/step
Epoch 40/1000
20/20 - 0s - loss: 0.9698 - categorical_accuracy: 0.6380 - val_loss: 0.9890 - val_categorical_accuracy: 0.6188 - 366ms/epoch - 18ms/step
Epoch 41/1000
20/20 - 0s - loss: 0.9842 - categorical_accuracy: 0.6308 - val_loss: 0.9119 - val_categorical_accuracy: 0.6666 - 372ms/epoch - 19ms/step
Epoch 42/1000
20/20 - 0s - loss: 1.0938 - categorical_accuracy: 0.6029 - val_loss: 0.9357 - val_categorical_accuracy: 0.6685 - 363ms/epoch - 18ms/step
Epoch 43/1000
20/20 - 0s - loss: 0.9438 - categorical_accuracy: 0.6505 - val_loss: 0.9292 - val_categorical_accuracy: 0.6630 - 363ms/epoch - 18ms/step
Epoch 44/1000
20/20 - 0s - loss: 0.9685 - categorical_accuracy: 0.6358 - val_loss: 0.8678 - val_categorical_accuracy: 0.6777 - 362ms/epoch - 18ms/step
Epoch 45/1000
20/20 - 0s - loss: 0.9382 - categorical_accuracy: 0.6463 - val_loss: 0.9706 - val_categorical_accuracy: 0.6440 - 367ms/epoch - 18ms/step
Epoch 46/1000
20/20 - 1s - loss: 0.9227 - categorical_accuracy: 0.6528 - val_loss: 1.0676 - val_categorical_accuracy: 0.6008 - 577ms/epoch - 29ms/step
Epoch 47/1000
20/20 - 0s - loss: 0.9114 - categorical_accuracy: 0.6587 - val_loss: 0.8296 - val_categorical_accuracy: 0.6903 - 371ms/epoch - 19ms/step
Epoch 48/1000
20/20 - 0s - loss: 0.9142 - categorical_accuracy: 0.6554 - val_loss: 0.8636 - val_categorical_accuracy: 0.6680 - 365ms/epoch - 18ms/step
Epoch 49/1000
20/20 - 0s - loss: 0.8824 - categorical_accuracy: 0.6666 - val_loss: 0.9755 - val_categorical_accuracy: 0.6369 - 368ms/epoch - 18ms/step
Epoch 50/1000
20/20 - 0s - loss: 0.9068 - categorical_accuracy: 0.6587 - val_loss: 0.8717 - val_categorical_accuracy: 0.6608 - 398ms/epoch - 20ms/step
Epoch 51/1000
20/20 - 0s - loss: 0.8770 - categorical_accuracy: 0.6689 - val_loss: 0.8681 - val_categorical_accuracy: 0.6662 - 383ms/epoch - 19ms/step
Epoch 52/1000
20/20 - 0s - loss: 0.8953 - categorical_accuracy: 0.6637 - val_loss: 0.8362 - val_categorical_accuracy: 0.6908 - 481ms/epoch - 24ms/step
Epoch 53/1000
20/20 - 1s - loss: 0.8238 - categorical_accuracy: 0.6895 - val_loss: 0.8657 - val_categorical_accuracy: 0.6676 - 560ms/epoch - 28ms/step
Epoch 54/1000
20/20 - 1s - loss: 0.8450 - categorical_accuracy: 0.6798 - val_loss: 0.8799 - val_categorical_accuracy: 0.6634 - 584ms/epoch - 29ms/step
Epoch 55/1000
20/20 - 2s - loss: 0.8555 - categorical_accuracy: 0.6809 - val_loss: 0.8288 - val_categorical_accuracy: 0.6767 - 2s/epoch - 76ms/step
Epoch 56/1000
20/20 - 3s - loss: 0.8472 - categorical_accuracy: 0.6786 - val_loss: 0.7613 - val_categorical_accuracy: 0.7117 - 3s/epoch - 163ms/step
Epoch 57/1000
20/20 - 3s - loss: 0.7912 - categorical_accuracy: 0.7003 - val_loss: 0.7869 - val_categorical_accuracy: 0.7072 - 3s/epoch - 160ms/step
Epoch 58/1000
20/20 - 3s - loss: 0.8151 - categorical_accuracy: 0.6939 - val_loss: 0.7730 - val_categorical_accuracy: 0.6987 - 3s/epoch - 164ms/step
Epoch 59/1000
20/20 - 3s - loss: 0.7878 - categorical_accuracy: 0.6995 - val_loss: 0.9211 - val_categorical_accuracy: 0.6480 - 3s/epoch - 126ms/step
Epoch 60/1000
20/20 - 6s - loss: 0.8400 - categorical_accuracy: 0.6882 - val_loss: 0.7111 - val_categorical_accuracy: 0.7333 - 6s/epoch - 290ms/step
Epoch 61/1000
20/20 - 3s - loss: 0.7761 - categorical_accuracy: 0.7092 - val_loss: 0.8085 - val_categorical_accuracy: 0.6930 - 3s/epoch - 126ms/step
Epoch 62/1000
20/20 - 3s - loss: 0.7555 - categorical_accuracy: 0.7096 - val_loss: 0.8112 - val_categorical_accuracy: 0.6882 - 3s/epoch - 136ms/step
Epoch 63/1000
20/20 - 2s - loss: 0.7877 - categorical_accuracy: 0.7029 - val_loss: 0.7067 - val_categorical_accuracy: 0.7340 - 2s/epoch - 96ms/step
Epoch 64/1000
20/20 - 3s - loss: 1.0458 - categorical_accuracy: 0.6513 - val_loss: 0.8234 - val_categorical_accuracy: 0.7109 - 3s/epoch - 148ms/step
Epoch 65/1000
20/20 - 19s - loss: 0.7256 - categorical_accuracy: 0.7337 - val_loss: 0.6989 - val_categorical_accuracy: 0.7310 - 19s/epoch - 969ms/step
Epoch 66/1000
20/20 - 0s - loss: 0.7362 - categorical_accuracy: 0.7183 - val_loss: 0.7244 - val_categorical_accuracy: 0.7172 - 345ms/epoch - 17ms/step
Epoch 67/1000
20/20 - 0s - loss: 0.7963 - categorical_accuracy: 0.7056 - val_loss: 0.6780 - val_categorical_accuracy: 0.7501 - 345ms/epoch - 17ms/step
Epoch 68/1000
20/20 - 0s - loss: 0.7026 - categorical_accuracy: 0.7334 - val_loss: 0.7187 - val_categorical_accuracy: 0.7265 - 357ms/epoch - 18ms/step
Epoch 69/1000
20/20 - 0s - loss: 0.7695 - categorical_accuracy: 0.7161 - val_loss: 0.6914 - val_categorical_accuracy: 0.7419 - 330ms/epoch - 17ms/step
Epoch 70/1000
20/20 - 0s - loss: 0.7597 - categorical_accuracy: 0.7254 - val_loss: 0.7027 - val_categorical_accuracy: 0.7338 - 349ms/epoch - 17ms/step
Epoch 71/1000
20/20 - 0s - loss: 0.6965 - categorical_accuracy: 0.7357 - val_loss: 0.7026 - val_categorical_accuracy: 0.7262 - 350ms/epoch - 18ms/step
Epoch 72/1000
20/20 - 0s - loss: 0.6965 - categorical_accuracy: 0.7331 - val_loss: 0.8882 - val_categorical_accuracy: 0.6803 - 366ms/epoch - 18ms/step
Epoch 73/1000
20/20 - 0s - loss: 0.7016 - categorical_accuracy: 0.7410 - val_loss: 0.6943 - val_categorical_accuracy: 0.7334 - 368ms/epoch - 18ms/step
Epoch 74/1000
20/20 - 0s - loss: 0.6904 - categorical_accuracy: 0.7360 - val_loss: 0.6893 - val_categorical_accuracy: 0.7320 - 364ms/epoch - 18ms/step
Epoch 75/1000
20/20 - 0s - loss: 0.7139 - categorical_accuracy: 0.7349 - val_loss: 0.6277 - val_categorical_accuracy: 0.7612 - 397ms/epoch - 20ms/step
Epoch 76/1000
20/20 - 0s - loss: 0.6643 - categorical_accuracy: 0.7449 - val_loss: 0.6492 - val_categorical_accuracy: 0.7471 - 369ms/epoch - 18ms/step
Epoch 77/1000
20/20 - 0s - loss: 0.7304 - categorical_accuracy: 0.7321 - val_loss: 0.6397 - val_categorical_accuracy: 0.7661 - 381ms/epoch - 19ms/step
Epoch 78/1000
20/20 - 0s - loss: 0.6322 - categorical_accuracy: 0.7634 - val_loss: 0.6881 - val_categorical_accuracy: 0.7370 - 386ms/epoch - 19ms/step
Epoch 79/1000
20/20 - 0s - loss: 0.9531 - categorical_accuracy: 0.6770 - val_loss: 0.6883 - val_categorical_accuracy: 0.7548 - 370ms/epoch - 19ms/step
Epoch 80/1000
20/20 - 0s - loss: 0.6284 - categorical_accuracy: 0.7683 - val_loss: 0.6027 - val_categorical_accuracy: 0.7770 - 357ms/epoch - 18ms/step
Epoch 81/1000
20/20 - 0s - loss: 0.6906 - categorical_accuracy: 0.7484 - val_loss: 0.6144 - val_categorical_accuracy: 0.7659 - 385ms/epoch - 19ms/step
Epoch 82/1000
20/20 - 0s - loss: 0.6405 - categorical_accuracy: 0.7569 - val_loss: 0.6399 - val_categorical_accuracy: 0.7536 - 366ms/epoch - 18ms/step
Epoch 83/1000
20/20 - 0s - loss: 0.6322 - categorical_accuracy: 0.7586 - val_loss: 0.6243 - val_categorical_accuracy: 0.7595 - 379ms/epoch - 19ms/step
Epoch 84/1000
20/20 - 0s - loss: 0.6151 - categorical_accuracy: 0.7676 - val_loss: 0.5982 - val_categorical_accuracy: 0.7777 - 373ms/epoch - 19ms/step
Epoch 85/1000
20/20 - 0s - loss: 0.6956 - categorical_accuracy: 0.7564 - val_loss: 0.5756 - val_categorical_accuracy: 0.7896 - 371ms/epoch - 19ms/step
Epoch 86/1000
20/20 - 0s - loss: 0.6099 - categorical_accuracy: 0.7693 - val_loss: 0.7913 - val_categorical_accuracy: 0.7028 - 365ms/epoch - 18ms/step
Epoch 87/1000
20/20 - 0s - loss: 0.6792 - categorical_accuracy: 0.7551 - val_loss: 0.6149 - val_categorical_accuracy: 0.7678 - 363ms/epoch - 18ms/step
Epoch 88/1000
20/20 - 0s - loss: 0.6051 - categorical_accuracy: 0.7715 - val_loss: 0.5664 - val_categorical_accuracy: 0.7883 - 365ms/epoch - 18ms/step
Epoch 89/1000
20/20 - 0s - loss: 0.6066 - categorical_accuracy: 0.7695 - val_loss: 0.8521 - val_categorical_accuracy: 0.6950 - 379ms/epoch - 19ms/step
Epoch 90/1000
20/20 - 0s - loss: 0.6460 - categorical_accuracy: 0.7652 - val_loss: 0.6680 - val_categorical_accuracy: 0.7415 - 368ms/epoch - 18ms/step
Epoch 91/1000
20/20 - 0s - loss: 0.5969 - categorical_accuracy: 0.7744 - val_loss: 0.6068 - val_categorical_accuracy: 0.7661 - 375ms/epoch - 19ms/step
Epoch 92/1000
20/20 - 0s - loss: 0.5823 - categorical_accuracy: 0.7797 - val_loss: 0.5797 - val_categorical_accuracy: 0.7771 - 367ms/epoch - 18ms/step
Epoch 93/1000
20/20 - 0s - loss: 0.5891 - categorical_accuracy: 0.7743 - val_loss: 0.5917 - val_categorical_accuracy: 0.7767 - 359ms/epoch - 18ms/step
Epoch 94/1000
20/20 - 0s - loss: 0.6973 - categorical_accuracy: 0.7540 - val_loss: 0.5461 - val_categorical_accuracy: 0.8013 - 348ms/epoch - 17ms/step
Epoch 95/1000
20/20 - 0s - loss: 0.5749 - categorical_accuracy: 0.7841 - val_loss: 0.6299 - val_categorical_accuracy: 0.7630 - 363ms/epoch - 18ms/step
Epoch 96/1000
20/20 - 0s - loss: 0.5722 - categorical_accuracy: 0.7825 - val_loss: 0.5713 - val_categorical_accuracy: 0.7913 - 386ms/epoch - 19ms/step
Epoch 97/1000
20/20 - 0s - loss: 0.6554 - categorical_accuracy: 0.7689 - val_loss: 0.5334 - val_categorical_accuracy: 0.8061 - 368ms/epoch - 18ms/step
Epoch 98/1000
20/20 - 0s - loss: 0.5483 - categorical_accuracy: 0.7913 - val_loss: 0.5548 - val_categorical_accuracy: 0.7902 - 364ms/epoch - 18ms/step
Epoch 99/1000
20/20 - 0s - loss: 0.5814 - categorical_accuracy: 0.7788 - val_loss: 0.5341 - val_categorical_accuracy: 0.7986 - 370ms/epoch - 19ms/step
Epoch 100/1000
20/20 - 0s - loss: 0.5540 - categorical_accuracy: 0.7910 - val_loss: 0.5540 - val_categorical_accuracy: 0.7913 - 351ms/epoch - 18ms/step
Epoch 101/1000
20/20 - 0s - loss: 0.5735 - categorical_accuracy: 0.7859 - val_loss: 1.0413 - val_categorical_accuracy: 0.6597 - 388ms/epoch - 19ms/step
Epoch 102/1000
20/20 - 0s - loss: 0.6126 - categorical_accuracy: 0.7865 - val_loss: 0.5678 - val_categorical_accuracy: 0.7848 - 371ms/epoch - 19ms/step
Epoch 103/1000
20/20 - 0s - loss: 0.5422 - categorical_accuracy: 0.7916 - val_loss: 0.5768 - val_categorical_accuracy: 0.7823 - 402ms/epoch - 20ms/step
Epoch 104/1000
20/20 - 0s - loss: 0.5661 - categorical_accuracy: 0.7876 - val_loss: 0.5403 - val_categorical_accuracy: 0.8034 - 372ms/epoch - 19ms/step
Epoch 105/1000
20/20 - 0s - loss: 0.5381 - categorical_accuracy: 0.7956 - val_loss: 0.5304 - val_categorical_accuracy: 0.7978 - 353ms/epoch - 18ms/step
Epoch 106/1000
20/20 - 0s - loss: 0.5432 - categorical_accuracy: 0.7903 - val_loss: 0.5851 - val_categorical_accuracy: 0.7772 - 393ms/epoch - 20ms/step
Epoch 107/1000
20/20 - 0s - loss: 0.5413 - categorical_accuracy: 0.7967 - val_loss: 0.5288 - val_categorical_accuracy: 0.7971 - 497ms/epoch - 25ms/step
Epoch 108/1000
20/20 - 0s - loss: 0.5896 - categorical_accuracy: 0.7812 - val_loss: 1.7639 - val_categorical_accuracy: 0.6357 - 401ms/epoch - 20ms/step
Epoch 109/1000
20/20 - 0s - loss: 0.6096 - categorical_accuracy: 0.7925 - val_loss: 0.4994 - val_categorical_accuracy: 0.8139 - 389ms/epoch - 19ms/step
Epoch 110/1000
20/20 - 0s - loss: 0.5138 - categorical_accuracy: 0.8059 - val_loss: 0.5885 - val_categorical_accuracy: 0.7774 - 374ms/epoch - 19ms/step
Epoch 111/1000
20/20 - 0s - loss: 0.5325 - categorical_accuracy: 0.8006 - val_loss: 0.5310 - val_categorical_accuracy: 0.7968 - 380ms/epoch - 19ms/step
Epoch 112/1000
20/20 - 0s - loss: 0.5078 - categorical_accuracy: 0.8045 - val_loss: 0.5005 - val_categorical_accuracy: 0.8145 - 394ms/epoch - 20ms/step
Epoch 113/1000
20/20 - 0s - loss: 0.5475 - categorical_accuracy: 0.7950 - val_loss: 0.5480 - val_categorical_accuracy: 0.8000 - 383ms/epoch - 19ms/step
Epoch 114/1000
20/20 - 0s - loss: 0.9282 - categorical_accuracy: 0.7229 - val_loss: 0.7456 - val_categorical_accuracy: 0.7407 - 400ms/epoch - 20ms/step
Epoch 115/1000
20/20 - 0s - loss: 0.5500 - categorical_accuracy: 0.8061 - val_loss: 0.4951 - val_categorical_accuracy: 0.8210 - 380ms/epoch - 19ms/step
Epoch 116/1000
20/20 - 0s - loss: 0.4977 - categorical_accuracy: 0.8136 - val_loss: 0.5279 - val_categorical_accuracy: 0.7978 - 394ms/epoch - 20ms/step
Epoch 117/1000
20/20 - 0s - loss: 0.5353 - categorical_accuracy: 0.7985 - val_loss: 0.4927 - val_categorical_accuracy: 0.8151 - 383ms/epoch - 19ms/step
Epoch 118/1000
20/20 - 0s - loss: 0.5023 - categorical_accuracy: 0.8108 - val_loss: 0.5394 - val_categorical_accuracy: 0.8031 - 381ms/epoch - 19ms/step
Epoch 119/1000
20/20 - 0s - loss: 0.6340 - categorical_accuracy: 0.7849 - val_loss: 0.4724 - val_categorical_accuracy: 0.8283 - 396ms/epoch - 20ms/step
Epoch 120/1000
20/20 - 0s - loss: 0.4861 - categorical_accuracy: 0.8161 - val_loss: 0.4875 - val_categorical_accuracy: 0.8162 - 392ms/epoch - 20ms/step
Epoch 121/1000
20/20 - 0s - loss: 0.5241 - categorical_accuracy: 0.8040 - val_loss: 0.4947 - val_categorical_accuracy: 0.8135 - 396ms/epoch - 20ms/step
Epoch 122/1000
20/20 - 0s - loss: 0.4793 - categorical_accuracy: 0.8184 - val_loss: 0.4748 - val_categorical_accuracy: 0.8223 - 383ms/epoch - 19ms/step
Epoch 123/1000
20/20 - 0s - loss: 0.5143 - categorical_accuracy: 0.8070 - val_loss: 0.4594 - val_categorical_accuracy: 0.8342 - 397ms/epoch - 20ms/step
Epoch 124/1000
20/20 - 0s - loss: 0.4832 - categorical_accuracy: 0.8182 - val_loss: 0.4831 - val_categorical_accuracy: 0.8165 - 390ms/epoch - 20ms/step
Epoch 125/1000
20/20 - 0s - loss: 0.4916 - categorical_accuracy: 0.8103 - val_loss: 0.5041 - val_categorical_accuracy: 0.8071 - 413ms/epoch - 21ms/step
Epoch 126/1000
20/20 - 0s - loss: 0.5002 - categorical_accuracy: 0.8143 - val_loss: 0.4963 - val_categorical_accuracy: 0.8109 - 405ms/epoch - 20ms/step
Epoch 127/1000
20/20 - 0s - loss: 0.4759 - categorical_accuracy: 0.8180 - val_loss: 0.4749 - val_categorical_accuracy: 0.8201 - 388ms/epoch - 19ms/step
Epoch 128/1000
20/20 - 0s - loss: 0.4738 - categorical_accuracy: 0.8208 - val_loss: 0.4769 - val_categorical_accuracy: 0.8194 - 396ms/epoch - 20ms/step
Epoch 129/1000
20/20 - 0s - loss: 0.4880 - categorical_accuracy: 0.8145 - val_loss: 0.5627 - val_categorical_accuracy: 0.7878 - 392ms/epoch - 20ms/step
Epoch 130/1000
20/20 - 0s - loss: 0.4824 - categorical_accuracy: 0.8203 - val_loss: 0.4792 - val_categorical_accuracy: 0.8220 - 385ms/epoch - 19ms/step
Epoch 131/1000
20/20 - 0s - loss: 0.4672 - categorical_accuracy: 0.8252 - val_loss: 0.4628 - val_categorical_accuracy: 0.8249 - 398ms/epoch - 20ms/step
Epoch 132/1000
20/20 - 0s - loss: 0.4863 - categorical_accuracy: 0.8143 - val_loss: 0.5474 - val_categorical_accuracy: 0.7942 - 393ms/epoch - 20ms/step
Epoch 133/1000
20/20 - 0s - loss: 0.4899 - categorical_accuracy: 0.8176 - val_loss: 0.4769 - val_categorical_accuracy: 0.8209 - 395ms/epoch - 20ms/step
Epoch 134/1000
20/20 - 0s - loss: 0.4516 - categorical_accuracy: 0.8295 - val_loss: 0.4747 - val_categorical_accuracy: 0.8173 - 391ms/epoch - 20ms/step
Epoch 135/1000
20/20 - 0s - loss: 0.4789 - categorical_accuracy: 0.8165 - val_loss: 0.6304 - val_categorical_accuracy: 0.7679 - 385ms/epoch - 19ms/step
Epoch 136/1000
20/20 - 0s - loss: 0.4773 - categorical_accuracy: 0.8250 - val_loss: 0.4317 - val_categorical_accuracy: 0.8419 - 399ms/epoch - 20ms/step
Epoch 137/1000
20/20 - 0s - loss: 0.4634 - categorical_accuracy: 0.8256 - val_loss: 0.4844 - val_categorical_accuracy: 0.8217 - 388ms/epoch - 19ms/step
Epoch 138/1000
20/20 - 0s - loss: 0.4568 - categorical_accuracy: 0.8303 - val_loss: 0.5247 - val_categorical_accuracy: 0.8005 - 388ms/epoch - 19ms/step
Epoch 139/1000
20/20 - 0s - loss: 0.4615 - categorical_accuracy: 0.8218 - val_loss: 0.4766 - val_categorical_accuracy: 0.8185 - 397ms/epoch - 20ms/step
Epoch 140/1000
20/20 - 0s - loss: 0.4600 - categorical_accuracy: 0.8269 - val_loss: 0.4726 - val_categorical_accuracy: 0.8261 - 388ms/epoch - 19ms/step
Epoch 141/1000
20/20 - 0s - loss: 0.4627 - categorical_accuracy: 0.8290 - val_loss: 0.4838 - val_categorical_accuracy: 0.8180 - 394ms/epoch - 20ms/step
Epoch 142/1000
20/20 - 0s - loss: 0.4540 - categorical_accuracy: 0.8285 - val_loss: 0.4339 - val_categorical_accuracy: 0.8452 - 378ms/epoch - 19ms/step
Epoch 143/1000
20/20 - 0s - loss: 0.4334 - categorical_accuracy: 0.8377 - val_loss: 0.4454 - val_categorical_accuracy: 0.8344 - 381ms/epoch - 19ms/step
Epoch 144/1000
20/20 - 0s - loss: 0.6018 - categorical_accuracy: 0.7919 - val_loss: 0.8283 - val_categorical_accuracy: 0.7112 - 394ms/epoch - 20ms/step
Epoch 145/1000
20/20 - 0s - loss: 0.4574 - categorical_accuracy: 0.8365 - val_loss: 0.4138 - val_categorical_accuracy: 0.8508 - 386ms/epoch - 19ms/step
Epoch 146/1000
20/20 - 0s - loss: 0.4382 - categorical_accuracy: 0.8349 - val_loss: 0.4294 - val_categorical_accuracy: 0.8410 - 405ms/epoch - 20ms/step
Epoch 147/1000
20/20 - 0s - loss: 0.4791 - categorical_accuracy: 0.8210 - val_loss: 0.4218 - val_categorical_accuracy: 0.8487 - 411ms/epoch - 21ms/step
Epoch 148/1000
20/20 - 0s - loss: 0.4607 - categorical_accuracy: 0.8307 - val_loss: 1.4186 - val_categorical_accuracy: 0.6888 - 396ms/epoch - 20ms/step
Epoch 149/1000
20/20 - 0s - loss: 0.5211 - categorical_accuracy: 0.8251 - val_loss: 0.4109 - val_categorical_accuracy: 0.8503 - 404ms/epoch - 20ms/step
Epoch 150/1000
20/20 - 0s - loss: 0.4337 - categorical_accuracy: 0.8361 - val_loss: 0.4253 - val_categorical_accuracy: 0.8423 - 390ms/epoch - 20ms/step
Epoch 151/1000
20/20 - 0s - loss: 0.4185 - categorical_accuracy: 0.8429 - val_loss: 0.4473 - val_categorical_accuracy: 0.8290 - 386ms/epoch - 19ms/step
Epoch 152/1000
20/20 - 0s - loss: 0.4258 - categorical_accuracy: 0.8381 - val_loss: 0.5012 - val_categorical_accuracy: 0.8092 - 392ms/epoch - 20ms/step
Epoch 153/1000
20/20 - 0s - loss: 0.4719 - categorical_accuracy: 0.8289 - val_loss: 0.4076 - val_categorical_accuracy: 0.8489 - 394ms/epoch - 20ms/step
Epoch 154/1000
20/20 - 0s - loss: 0.4206 - categorical_accuracy: 0.8407 - val_loss: 0.4272 - val_categorical_accuracy: 0.8414 - 403ms/epoch - 20ms/step
Epoch 155/1000
20/20 - 0s - loss: 0.4222 - categorical_accuracy: 0.8394 - val_loss: 0.4765 - val_categorical_accuracy: 0.8217 - 396ms/epoch - 20ms/step
Epoch 156/1000
20/20 - 0s - loss: 0.4664 - categorical_accuracy: 0.8263 - val_loss: 0.4036 - val_categorical_accuracy: 0.8526 - 406ms/epoch - 20ms/step
Epoch 157/1000
20/20 - 0s - loss: 0.4131 - categorical_accuracy: 0.8455 - val_loss: 0.4500 - val_categorical_accuracy: 0.8290 - 413ms/epoch - 21ms/step
Epoch 158/1000
20/20 - 0s - loss: 0.4143 - categorical_accuracy: 0.8428 - val_loss: 0.4157 - val_categorical_accuracy: 0.8449 - 401ms/epoch - 20ms/step
Epoch 159/1000
20/20 - 0s - loss: 0.4665 - categorical_accuracy: 0.8233 - val_loss: 0.4292 - val_categorical_accuracy: 0.8466 - 396ms/epoch - 20ms/step
Epoch 160/1000
20/20 - 0s - loss: 0.3927 - categorical_accuracy: 0.8560 - val_loss: 0.4966 - val_categorical_accuracy: 0.8151 - 395ms/epoch - 20ms/step
Epoch 161/1000
20/20 - 0s - loss: 0.4201 - categorical_accuracy: 0.8407 - val_loss: 0.4317 - val_categorical_accuracy: 0.8366 - 417ms/epoch - 21ms/step
Epoch 162/1000
20/20 - 0s - loss: 0.3995 - categorical_accuracy: 0.8519 - val_loss: 0.4699 - val_categorical_accuracy: 0.8313 - 393ms/epoch - 20ms/step
Epoch 163/1000
20/20 - 0s - loss: 1.1788 - categorical_accuracy: 0.6886 - val_loss: 0.4891 - val_categorical_accuracy: 0.8317 - 397ms/epoch - 20ms/step
Epoch 164/1000
20/20 - 0s - loss: 0.4261 - categorical_accuracy: 0.8508 - val_loss: 0.4066 - val_categorical_accuracy: 0.8542 - 406ms/epoch - 20ms/step
Epoch 165/1000
20/20 - 0s - loss: 0.4150 - categorical_accuracy: 0.8507 - val_loss: 0.5690 - val_categorical_accuracy: 0.7940 - 384ms/epoch - 19ms/step
Epoch 166/1000
20/20 - 0s - loss: 0.4065 - categorical_accuracy: 0.8513 - val_loss: 0.4292 - val_categorical_accuracy: 0.8395 - 428ms/epoch - 21ms/step
Epoch 167/1000
20/20 - 0s - loss: 0.4218 - categorical_accuracy: 0.8398 - val_loss: 0.3986 - val_categorical_accuracy: 0.8522 - 418ms/epoch - 21ms/step
Epoch 168/1000
20/20 - 4s - loss: 0.3975 - categorical_accuracy: 0.8510 - val_loss: 0.4014 - val_categorical_accuracy: 0.8526 - 4s/epoch - 198ms/step
Epoch 169/1000
20/20 - 2s - loss: 0.3982 - categorical_accuracy: 0.8508 - val_loss: 0.4115 - val_categorical_accuracy: 0.8477 - 2s/epoch - 113ms/step
Epoch 170/1000
20/20 - 2s - loss: 0.4493 - categorical_accuracy: 0.8375 - val_loss: 0.3765 - val_categorical_accuracy: 0.8658 - 2s/epoch - 107ms/step
Epoch 171/1000
20/20 - 3s - loss: 0.4003 - categorical_accuracy: 0.8488 - val_loss: 0.4254 - val_categorical_accuracy: 0.8402 - 3s/epoch - 141ms/step
Epoch 172/1000
20/20 - 2s - loss: 0.3912 - categorical_accuracy: 0.8535 - val_loss: 0.4186 - val_categorical_accuracy: 0.8435 - 2s/epoch - 108ms/step
Epoch 173/1000
20/20 - 1s - loss: 0.3826 - categorical_accuracy: 0.8570 - val_loss: 0.3964 - val_categorical_accuracy: 0.8563 - 829ms/epoch - 41ms/step
Epoch 174/1000
20/20 - 1s - loss: 0.4476 - categorical_accuracy: 0.8353 - val_loss: 0.3868 - val_categorical_accuracy: 0.8610 - 806ms/epoch - 40ms/step
Epoch 175/1000
20/20 - 1s - loss: 0.3949 - categorical_accuracy: 0.8509 - val_loss: 0.4200 - val_categorical_accuracy: 0.8400 - 841ms/epoch - 42ms/step
Epoch 176/1000
20/20 - 1s - loss: 0.3834 - categorical_accuracy: 0.8562 - val_loss: 0.4308 - val_categorical_accuracy: 0.8382 - 783ms/epoch - 39ms/step
Epoch 177/1000
20/20 - 1s - loss: 0.3718 - categorical_accuracy: 0.8618 - val_loss: 0.3940 - val_categorical_accuracy: 0.8585 - 769ms/epoch - 38ms/step
Epoch 178/1000
20/20 - 1s - loss: 0.4539 - categorical_accuracy: 0.8341 - val_loss: 0.3701 - val_categorical_accuracy: 0.8667 - 850ms/epoch - 42ms/step
Epoch 179/1000
20/20 - 1s - loss: 0.3646 - categorical_accuracy: 0.8660 - val_loss: 0.3973 - val_categorical_accuracy: 0.8541 - 832ms/epoch - 42ms/step
Epoch 180/1000
20/20 - 1s - loss: 0.4023 - categorical_accuracy: 0.8477 - val_loss: 0.4166 - val_categorical_accuracy: 0.8434 - 912ms/epoch - 46ms/step
Epoch 181/1000
20/20 - 1s - loss: 0.3647 - categorical_accuracy: 0.8666 - val_loss: 0.4185 - val_categorical_accuracy: 0.8447 - 792ms/epoch - 40ms/step
Epoch 182/1000
20/20 - 1s - loss: 0.4057 - categorical_accuracy: 0.8495 - val_loss: 0.4417 - val_categorical_accuracy: 0.8338 - 879ms/epoch - 44ms/step
Epoch 183/1000
20/20 - 1s - loss: 0.3902 - categorical_accuracy: 0.8510 - val_loss: 0.3835 - val_categorical_accuracy: 0.8590 - 820ms/epoch - 41ms/step
Epoch 184/1000
20/20 - 11s - loss: 0.3652 - categorical_accuracy: 0.8643 - val_loss: 0.3840 - val_categorical_accuracy: 0.8575 - 11s/epoch - 525ms/step
Epoch 185/1000
20/20 - 1s - loss: 0.9946 - categorical_accuracy: 0.7245 - val_loss: 0.5649 - val_categorical_accuracy: 0.8088 - 813ms/epoch - 41ms/step
Epoch 186/1000
20/20 - 1s - loss: 0.4321 - categorical_accuracy: 0.8523 - val_loss: 0.3856 - val_categorical_accuracy: 0.8641 - 883ms/epoch - 44ms/step
Epoch 187/1000
20/20 - 1s - loss: 0.3559 - categorical_accuracy: 0.8744 - val_loss: 0.3763 - val_categorical_accuracy: 0.8615 - 788ms/epoch - 39ms/step
Epoch 188/1000
20/20 - 1s - loss: 0.3989 - categorical_accuracy: 0.8544 - val_loss: 0.3822 - val_categorical_accuracy: 0.8587 - 782ms/epoch - 39ms/step
Epoch 189/1000
20/20 - 1s - loss: 0.3876 - categorical_accuracy: 0.8537 - val_loss: 0.3661 - val_categorical_accuracy: 0.8661 - 768ms/epoch - 38ms/step
Epoch 190/1000
20/20 - 1s - loss: 0.3605 - categorical_accuracy: 0.8670 - val_loss: 0.3593 - val_categorical_accuracy: 0.8687 - 761ms/epoch - 38ms/step
Epoch 191/1000
20/20 - 2s - loss: 0.3864 - categorical_accuracy: 0.8566 - val_loss: 0.4862 - val_categorical_accuracy: 0.8198 - 2s/epoch - 78ms/step
Epoch 192/1000
20/20 - 2s - loss: 0.4006 - categorical_accuracy: 0.8501 - val_loss: 0.3751 - val_categorical_accuracy: 0.8604 - 2s/epoch - 103ms/step
Epoch 193/1000
20/20 - 1s - loss: 0.3611 - categorical_accuracy: 0.8655 - val_loss: 0.3971 - val_categorical_accuracy: 0.8530 - 747ms/epoch - 37ms/step
Epoch 194/1000
20/20 - 1s - loss: 0.3671 - categorical_accuracy: 0.8626 - val_loss: 0.3873 - val_categorical_accuracy: 0.8559 - 788ms/epoch - 39ms/step
Epoch 195/1000
20/20 - 1s - loss: 0.4125 - categorical_accuracy: 0.8500 - val_loss: 0.3531 - val_categorical_accuracy: 0.8729 - 860ms/epoch - 43ms/step
Epoch 196/1000
20/20 - 1s - loss: 0.3649 - categorical_accuracy: 0.8640 - val_loss: 0.3861 - val_categorical_accuracy: 0.8555 - 787ms/epoch - 39ms/step
Epoch 197/1000
20/20 - 1s - loss: 0.3573 - categorical_accuracy: 0.8666 - val_loss: 0.3674 - val_categorical_accuracy: 0.8646 - 1s/epoch - 55ms/step
Epoch 198/1000
20/20 - 1s - loss: 0.3583 - categorical_accuracy: 0.8667 - val_loss: 0.3590 - val_categorical_accuracy: 0.8692 - 838ms/epoch - 42ms/step
Epoch 199/1000
20/20 - 1s - loss: 0.3491 - categorical_accuracy: 0.8718 - val_loss: 0.4026 - val_categorical_accuracy: 0.8494 - 926ms/epoch - 46ms/step
Epoch 200/1000
20/20 - 0s - loss: 0.3823 - categorical_accuracy: 0.8546 - val_loss: 0.3634 - val_categorical_accuracy: 0.8656 - 458ms/epoch - 23ms/step
Epoch 201/1000
20/20 - 0s - loss: 0.3419 - categorical_accuracy: 0.8733 - val_loss: 0.4319 - val_categorical_accuracy: 0.8401 - 443ms/epoch - 22ms/step
Epoch 202/1000
20/20 - 0s - loss: 0.4290 - categorical_accuracy: 0.8476 - val_loss: 0.3873 - val_categorical_accuracy: 0.8566 - 438ms/epoch - 22ms/step
Epoch 203/1000
20/20 - 1s - loss: 0.3553 - categorical_accuracy: 0.8677 - val_loss: 0.3607 - val_categorical_accuracy: 0.8678 - 507ms/epoch - 25ms/step
Epoch 204/1000
20/20 - 1s - loss: 0.3448 - categorical_accuracy: 0.8724 - val_loss: 0.3368 - val_categorical_accuracy: 0.8800 - 550ms/epoch - 27ms/step
Epoch 205/1000
20/20 - 1s - loss: 0.3649 - categorical_accuracy: 0.8629 - val_loss: 0.3715 - val_categorical_accuracy: 0.8605 - 886ms/epoch - 44ms/step
Epoch 206/1000
20/20 - 3s - loss: 0.3536 - categorical_accuracy: 0.8675 - val_loss: 0.3762 - val_categorical_accuracy: 0.8583 - 3s/epoch - 163ms/step
Epoch 207/1000
20/20 - 2s - loss: 0.4176 - categorical_accuracy: 0.8519 - val_loss: 0.3365 - val_categorical_accuracy: 0.8805 - 2s/epoch - 111ms/step
Epoch 208/1000
20/20 - 1s - loss: 0.3364 - categorical_accuracy: 0.8763 - val_loss: 0.3610 - val_categorical_accuracy: 0.8659 - 900ms/epoch - 45ms/step
Epoch 209/1000
20/20 - 1s - loss: 0.3539 - categorical_accuracy: 0.8680 - val_loss: 0.4059 - val_categorical_accuracy: 0.8457 - 878ms/epoch - 44ms/step
Epoch 210/1000
20/20 - 1s - loss: 0.3357 - categorical_accuracy: 0.8750 - val_loss: 0.3688 - val_categorical_accuracy: 0.8623 - 809ms/epoch - 40ms/step
Epoch 211/1000
20/20 - 1s - loss: 0.3473 - categorical_accuracy: 0.8705 - val_loss: 0.3441 - val_categorical_accuracy: 0.8759 - 825ms/epoch - 41ms/step
Epoch 212/1000
20/20 - 1s - loss: 0.4041 - categorical_accuracy: 0.8559 - val_loss: 0.3292 - val_categorical_accuracy: 0.8839 - 829ms/epoch - 41ms/step
Epoch 213/1000
20/20 - 1s - loss: 0.3036 - categorical_accuracy: 0.8929 - val_loss: 0.3545 - val_categorical_accuracy: 0.8690 - 856ms/epoch - 43ms/step
Epoch 214/1000
20/20 - 1s - loss: 0.3654 - categorical_accuracy: 0.8617 - val_loss: 0.3761 - val_categorical_accuracy: 0.8587 - 857ms/epoch - 43ms/step
Epoch 215/1000
20/20 - 1s - loss: 0.3407 - categorical_accuracy: 0.8725 - val_loss: 0.3664 - val_categorical_accuracy: 0.8630 - 784ms/epoch - 39ms/step
Epoch 216/1000
20/20 - 1s - loss: 0.3405 - categorical_accuracy: 0.8725 - val_loss: 0.3324 - val_categorical_accuracy: 0.8797 - 819ms/epoch - 41ms/step
Epoch 217/1000
20/20 - 1s - loss: 0.3406 - categorical_accuracy: 0.8736 - val_loss: 0.3757 - val_categorical_accuracy: 0.8649 - 762ms/epoch - 38ms/step
Epoch 218/1000
20/20 - 1s - loss: 1.2344 - categorical_accuracy: 0.6833 - val_loss: 0.5065 - val_categorical_accuracy: 0.8286 - 842ms/epoch - 42ms/step
Epoch 219/1000
20/20 - 1s - loss: 0.3929 - categorical_accuracy: 0.8667 - val_loss: 0.3588 - val_categorical_accuracy: 0.8749 - 707ms/epoch - 35ms/step
Epoch 220/1000
20/20 - 1s - loss: 0.3248 - categorical_accuracy: 0.8860 - val_loss: 0.3593 - val_categorical_accuracy: 0.8683 - 745ms/epoch - 37ms/step
Epoch 221/1000
20/20 - 1s - loss: 0.3433 - categorical_accuracy: 0.8736 - val_loss: 0.3432 - val_categorical_accuracy: 0.8768 - 769ms/epoch - 38ms/step
Epoch 222/1000
20/20 - 1s - loss: 0.3286 - categorical_accuracy: 0.8802 - val_loss: 0.3603 - val_categorical_accuracy: 0.8667 - 786ms/epoch - 39ms/step
Epoch 223/1000
20/20 - 1s - loss: 0.3360 - categorical_accuracy: 0.8746 - val_loss: 0.3337 - val_categorical_accuracy: 0.8796 - 786ms/epoch - 39ms/step
Epoch 224/1000
20/20 - 1s - loss: 0.4187 - categorical_accuracy: 0.8581 - val_loss: 0.3262 - val_categorical_accuracy: 0.8859 - 737ms/epoch - 37ms/step
Epoch 225/1000
20/20 - 1s - loss: 0.3061 - categorical_accuracy: 0.8916 - val_loss: 0.3803 - val_categorical_accuracy: 0.8613 - 752ms/epoch - 38ms/step
Epoch 226/1000
20/20 - 1s - loss: 0.3521 - categorical_accuracy: 0.8676 - val_loss: 0.3279 - val_categorical_accuracy: 0.8816 - 873ms/epoch - 44ms/step
Epoch 227/1000
20/20 - 1s - loss: 0.3155 - categorical_accuracy: 0.8836 - val_loss: 0.3374 - val_categorical_accuracy: 0.8764 - 821ms/epoch - 41ms/step
Epoch 228/1000
20/20 - 1s - loss: 0.3254 - categorical_accuracy: 0.8789 - val_loss: 0.3881 - val_categorical_accuracy: 0.8541 - 708ms/epoch - 35ms/step
Epoch 229/1000
20/20 - 1s - loss: 0.3258 - categorical_accuracy: 0.8805 - val_loss: 0.3522 - val_categorical_accuracy: 0.8706 - 748ms/epoch - 37ms/step
Epoch 230/1000
20/20 - 1s - loss: 0.3224 - categorical_accuracy: 0.8826 - val_loss: 0.4049 - val_categorical_accuracy: 0.8497 - 760ms/epoch - 38ms/step
Epoch 231/1000
20/20 - 1s - loss: 0.3364 - categorical_accuracy: 0.8761 - val_loss: 0.4675 - val_categorical_accuracy: 0.8278 - 704ms/epoch - 35ms/step
Epoch 232/1000
20/20 - 1s - loss: 0.3833 - categorical_accuracy: 0.8617 - val_loss: 0.3355 - val_categorical_accuracy: 0.8767 - 708ms/epoch - 35ms/step
Epoch 233/1000
20/20 - 1s - loss: 0.3337 - categorical_accuracy: 0.8758 - val_loss: 0.3538 - val_categorical_accuracy: 0.8680 - 683ms/epoch - 34ms/step
Epoch 234/1000
20/20 - 1s - loss: 0.2979 - categorical_accuracy: 0.8925 - val_loss: 0.3088 - val_categorical_accuracy: 0.8896 - 695ms/epoch - 35ms/step
Epoch 235/1000
20/20 - 1s - loss: 0.3058 - categorical_accuracy: 0.8891 - val_loss: 0.3324 - val_categorical_accuracy: 0.8782 - 687ms/epoch - 34ms/step
Epoch 236/1000
20/20 - 1s - loss: 0.3412 - categorical_accuracy: 0.8712 - val_loss: 0.3285 - val_categorical_accuracy: 0.8790 - 806ms/epoch - 40ms/step
Epoch 237/1000
20/20 - 1s - loss: 0.3178 - categorical_accuracy: 0.8832 - val_loss: 0.3068 - val_categorical_accuracy: 0.8898 - 738ms/epoch - 37ms/step
Epoch 238/1000
20/20 - 1s - loss: 0.3376 - categorical_accuracy: 0.8757 - val_loss: 0.6023 - val_categorical_accuracy: 0.7989 - 687ms/epoch - 34ms/step
Epoch 239/1000
20/20 - 1s - loss: 0.3700 - categorical_accuracy: 0.8739 - val_loss: 0.3346 - val_categorical_accuracy: 0.8772 - 745ms/epoch - 37ms/step
Epoch 240/1000
20/20 - 1s - loss: 0.3235 - categorical_accuracy: 0.8791 - val_loss: 0.3551 - val_categorical_accuracy: 0.8666 - 761ms/epoch - 38ms/step
Epoch 241/1000
20/20 - 1s - loss: 0.3230 - categorical_accuracy: 0.8793 - val_loss: 0.3288 - val_categorical_accuracy: 0.8803 - 906ms/epoch - 45ms/step
Epoch 242/1000
20/20 - 1s - loss: 0.2953 - categorical_accuracy: 0.8927 - val_loss: 0.3266 - val_categorical_accuracy: 0.8808 - 792ms/epoch - 40ms/step
Epoch 243/1000
20/20 - 1s - loss: 0.3111 - categorical_accuracy: 0.8856 - val_loss: 0.3752 - val_categorical_accuracy: 0.8616 - 683ms/epoch - 34ms/step
Epoch 244/1000
20/20 - 1s - loss: 0.3377 - categorical_accuracy: 0.8745 - val_loss: 0.3064 - val_categorical_accuracy: 0.8908 - 677ms/epoch - 34ms/step
Epoch 245/1000
20/20 - 1s - loss: 0.2947 - categorical_accuracy: 0.8931 - val_loss: 0.3213 - val_categorical_accuracy: 0.8837 - 680ms/epoch - 34ms/step
Epoch 246/1000
20/20 - 1s - loss: 0.3289 - categorical_accuracy: 0.8794 - val_loss: 0.3171 - val_categorical_accuracy: 0.8845 - 639ms/epoch - 32ms/step
Epoch 247/1000
20/20 - 1s - loss: 0.3269 - categorical_accuracy: 0.8810 - val_loss: 0.3803 - val_categorical_accuracy: 0.8582 - 707ms/epoch - 35ms/step
Epoch 248/1000
20/20 - 1s - loss: 0.3153 - categorical_accuracy: 0.8838 - val_loss: 0.3743 - val_categorical_accuracy: 0.8586 - 661ms/epoch - 33ms/step
Epoch 249/1000
20/20 - 1s - loss: 0.3070 - categorical_accuracy: 0.8876 - val_loss: 0.3304 - val_categorical_accuracy: 0.8786 - 689ms/epoch - 34ms/step
Epoch 250/1000
20/20 - 1s - loss: 0.2911 - categorical_accuracy: 0.8942 - val_loss: 0.3326 - val_categorical_accuracy: 0.8773 - 628ms/epoch - 31ms/step
Epoch 251/1000
20/20 - 1s - loss: 0.2969 - categorical_accuracy: 0.8915 - val_loss: 0.3354 - val_categorical_accuracy: 0.8765 - 649ms/epoch - 32ms/step
Epoch 252/1000
20/20 - 1s - loss: 0.3273 - categorical_accuracy: 0.8797 - val_loss: 0.2953 - val_categorical_accuracy: 0.8943 - 656ms/epoch - 33ms/step
Epoch 253/1000
20/20 - 1s - loss: 1.2789 - categorical_accuracy: 0.6736 - val_loss: 0.8307 - val_categorical_accuracy: 0.7330 - 676ms/epoch - 34ms/step
Epoch 254/1000
20/20 - 1s - loss: 0.5042 - categorical_accuracy: 0.8317 - val_loss: 0.3669 - val_categorical_accuracy: 0.8743 - 677ms/epoch - 34ms/step
Epoch 255/1000
20/20 - 1s - loss: 0.3151 - categorical_accuracy: 0.8915 - val_loss: 0.3129 - val_categorical_accuracy: 0.8902 - 674ms/epoch - 34ms/step
Epoch 256/1000
20/20 - 1s - loss: 0.2855 - categorical_accuracy: 0.8997 - val_loss: 0.3162 - val_categorical_accuracy: 0.8856 - 675ms/epoch - 34ms/step
Epoch 257/1000
20/20 - 1s - loss: 0.3129 - categorical_accuracy: 0.8866 - val_loss: 0.3329 - val_categorical_accuracy: 0.8790 - 662ms/epoch - 33ms/step
Epoch 258/1000
20/20 - 1s - loss: 0.3123 - categorical_accuracy: 0.8865 - val_loss: 0.3049 - val_categorical_accuracy: 0.8906 - 755ms/epoch - 38ms/step
Epoch 259/1000
20/20 - 1s - loss: 0.2696 - categorical_accuracy: 0.9049 - val_loss: 0.2902 - val_categorical_accuracy: 0.8980 - 788ms/epoch - 39ms/step
Epoch 260/1000
20/20 - 1s - loss: 0.4045 - categorical_accuracy: 0.8654 - val_loss: 0.2947 - val_categorical_accuracy: 0.8963 - 736ms/epoch - 37ms/step
Epoch 261/1000
20/20 - 1s - loss: 0.2823 - categorical_accuracy: 0.8997 - val_loss: 0.3814 - val_categorical_accuracy: 0.8579 - 796ms/epoch - 40ms/step
Epoch 262/1000
20/20 - 1s - loss: 0.3088 - categorical_accuracy: 0.8853 - val_loss: 0.3246 - val_categorical_accuracy: 0.8813 - 783ms/epoch - 39ms/step
Epoch 263/1000
20/20 - 1s - loss: 0.2940 - categorical_accuracy: 0.8921 - val_loss: 0.3024 - val_categorical_accuracy: 0.8902 - 888ms/epoch - 44ms/step
Epoch 264/1000
20/20 - 1s - loss: 0.2799 - categorical_accuracy: 0.8986 - val_loss: 0.2884 - val_categorical_accuracy: 0.8969 - 994ms/epoch - 50ms/step
Epoch 265/1000
20/20 - 0s - loss: 0.3176 - categorical_accuracy: 0.8831 - val_loss: 0.3083 - val_categorical_accuracy: 0.8887 - 495ms/epoch - 25ms/step
Epoch 266/1000
20/20 - 0s - loss: 0.2752 - categorical_accuracy: 0.9012 - val_loss: 0.3466 - val_categorical_accuracy: 0.8725 - 456ms/epoch - 23ms/step
Epoch 267/1000
20/20 - 0s - loss: 0.2946 - categorical_accuracy: 0.8930 - val_loss: 0.3512 - val_categorical_accuracy: 0.8706 - 498ms/epoch - 25ms/step
Epoch 268/1000
20/20 - 0s - loss: 0.3120 - categorical_accuracy: 0.8845 - val_loss: 0.2860 - val_categorical_accuracy: 0.8992 - 466ms/epoch - 23ms/step
Epoch 269/1000
20/20 - 1s - loss: 0.3701 - categorical_accuracy: 0.8702 - val_loss: 0.3190 - val_categorical_accuracy: 0.8879 - 1s/epoch - 56ms/step
Epoch 270/1000
20/20 - 1s - loss: 0.2744 - categorical_accuracy: 0.9033 - val_loss: 0.3533 - val_categorical_accuracy: 0.8701 - 878ms/epoch - 44ms/step
Epoch 271/1000
20/20 - 1s - loss: 0.2916 - categorical_accuracy: 0.8930 - val_loss: 0.3177 - val_categorical_accuracy: 0.8800 - 815ms/epoch - 41ms/step
Epoch 272/1000
20/20 - 1s - loss: 0.2743 - categorical_accuracy: 0.8996 - val_loss: 0.3001 - val_categorical_accuracy: 0.8907 - 827ms/epoch - 41ms/step
Epoch 273/1000
20/20 - 1s - loss: 0.3107 - categorical_accuracy: 0.8847 - val_loss: 0.3010 - val_categorical_accuracy: 0.8910 - 1s/epoch - 52ms/step
Epoch 274/1000
20/20 - 1s - loss: 0.2635 - categorical_accuracy: 0.9061 - val_loss: 0.3009 - val_categorical_accuracy: 0.8902 - 958ms/epoch - 48ms/step
Epoch 275/1000
20/20 - 1s - loss: 0.2949 - categorical_accuracy: 0.8905 - val_loss: 0.3431 - val_categorical_accuracy: 0.8731 - 1s/epoch - 52ms/step
Epoch 276/1000
20/20 - 1s - loss: 0.2841 - categorical_accuracy: 0.8960 - val_loss: 0.3352 - val_categorical_accuracy: 0.8781 - 748ms/epoch - 37ms/step
Epoch 277/1000
20/20 - 1s - loss: 0.4180 - categorical_accuracy: 0.8702 - val_loss: 0.2827 - val_categorical_accuracy: 0.8995 - 653ms/epoch - 33ms/step
Epoch 278/1000
20/20 - 1s - loss: 0.2598 - categorical_accuracy: 0.9086 - val_loss: 0.3360 - val_categorical_accuracy: 0.8805 - 690ms/epoch - 35ms/step
Epoch 279/1000
20/20 - 1s - loss: 0.3025 - categorical_accuracy: 0.8884 - val_loss: 0.2892 - val_categorical_accuracy: 0.8965 - 919ms/epoch - 46ms/step
Epoch 280/1000
20/20 - 1s - loss: 0.2781 - categorical_accuracy: 0.8985 - val_loss: 0.2914 - val_categorical_accuracy: 0.8940 - 924ms/epoch - 46ms/step
Epoch 281/1000
20/20 - 1s - loss: 0.2637 - categorical_accuracy: 0.9047 - val_loss: 0.3106 - val_categorical_accuracy: 0.8862 - 783ms/epoch - 39ms/step
Epoch 282/1000
20/20 - 1s - loss: 0.3081 - categorical_accuracy: 0.8858 - val_loss: 0.2948 - val_categorical_accuracy: 0.8936 - 871ms/epoch - 44ms/step
Epoch 283/1000
20/20 - 1s - loss: 0.2584 - categorical_accuracy: 0.9075 - val_loss: 0.3134 - val_categorical_accuracy: 0.8849 - 757ms/epoch - 38ms/step
Epoch 284/1000
20/20 - 1s - loss: 0.2742 - categorical_accuracy: 0.8995 - val_loss: 0.3544 - val_categorical_accuracy: 0.8706 - 719ms/epoch - 36ms/step
Epoch 285/1000
20/20 - 1s - loss: 0.3039 - categorical_accuracy: 0.8887 - val_loss: 0.2932 - val_categorical_accuracy: 0.8965 - 688ms/epoch - 34ms/step
Epoch 286/1000
20/20 - 11s - loss: 0.2543 - categorical_accuracy: 0.9099 - val_loss: 0.3232 - val_categorical_accuracy: 0.8812 - 11s/epoch - 553ms/step
Epoch 287/1000
20/20 - 3s - loss: 0.3773 - categorical_accuracy: 0.8657 - val_loss: 0.4026 - val_categorical_accuracy: 0.8581 - 3s/epoch - 148ms/step
Epoch 288/1000
20/20 - 14s - loss: 0.2617 - categorical_accuracy: 0.9093 - val_loss: 0.3325 - val_categorical_accuracy: 0.8775 - 14s/epoch - 715ms/step
Epoch 289/1000
20/20 - 10s - loss: 0.2876 - categorical_accuracy: 0.8941 - val_loss: 0.2749 - val_categorical_accuracy: 0.9022 - 10s/epoch - 491ms/step
Epoch 290/1000
20/20 - 1s - loss: 0.2847 - categorical_accuracy: 0.8941 - val_loss: 0.2992 - val_categorical_accuracy: 0.8897 - 963ms/epoch - 48ms/step
Epoch 291/1000
20/20 - 1s - loss: 0.2513 - categorical_accuracy: 0.9110 - val_loss: 0.3189 - val_categorical_accuracy: 0.8826 - 626ms/epoch - 31ms/step
Epoch 292/1000
20/20 - 1s - loss: 0.2769 - categorical_accuracy: 0.8999 - val_loss: 0.2568 - val_categorical_accuracy: 0.9099 - 562ms/epoch - 28ms/step
Epoch 293/1000
20/20 - 0s - loss: 0.2579 - categorical_accuracy: 0.9076 - val_loss: 0.3097 - val_categorical_accuracy: 0.8879 - 496ms/epoch - 25ms/step
Epoch 294/1000
20/20 - 1s - loss: 1.6453 - categorical_accuracy: 0.6108 - val_loss: 0.8161 - val_categorical_accuracy: 0.7311 - 637ms/epoch - 32ms/step
Epoch 295/1000
20/20 - 1s - loss: 0.5732 - categorical_accuracy: 0.8019 - val_loss: 0.4349 - val_categorical_accuracy: 0.8459 - 610ms/epoch - 30ms/step
Epoch 296/1000
20/20 - 1s - loss: 0.3682 - categorical_accuracy: 0.8713 - val_loss: 0.3529 - val_categorical_accuracy: 0.8747 - 611ms/epoch - 31ms/step
Epoch 297/1000
20/20 - 1s - loss: 0.3139 - categorical_accuracy: 0.8880 - val_loss: 0.3373 - val_categorical_accuracy: 0.8776 - 566ms/epoch - 28ms/step
Epoch 298/1000
20/20 - 1s - loss: 0.3084 - categorical_accuracy: 0.8889 - val_loss: 0.2944 - val_categorical_accuracy: 0.8956 - 655ms/epoch - 33ms/step
Epoch 299/1000
20/20 - 1s - loss: 0.2887 - categorical_accuracy: 0.8964 - val_loss: 0.3236 - val_categorical_accuracy: 0.8811 - 640ms/epoch - 32ms/step
Epoch 300/1000
20/20 - 1s - loss: 0.2809 - categorical_accuracy: 0.8993 - val_loss: 0.3014 - val_categorical_accuracy: 0.8928 - 632ms/epoch - 32ms/step
Epoch 301/1000
20/20 - 1s - loss: 0.3697 - categorical_accuracy: 0.8761 - val_loss: 0.2757 - val_categorical_accuracy: 0.9036 - 703ms/epoch - 35ms/step
Epoch 302/1000
20/20 - 1s - loss: 0.2590 - categorical_accuracy: 0.9092 - val_loss: 0.3635 - val_categorical_accuracy: 0.8667 - 651ms/epoch - 33ms/step
Epoch 303/1000
20/20 - 1s - loss: 0.2787 - categorical_accuracy: 0.8990 - val_loss: 0.2820 - val_categorical_accuracy: 0.9000 - 660ms/epoch - 33ms/step
Epoch 304/1000
20/20 - 1s - loss: 0.2705 - categorical_accuracy: 0.9023 - val_loss: 0.2983 - val_categorical_accuracy: 0.8905 - 715ms/epoch - 36ms/step
Epoch 305/1000
20/20 - 1s - loss: 0.2626 - categorical_accuracy: 0.9048 - val_loss: 0.2717 - val_categorical_accuracy: 0.9039 - 691ms/epoch - 35ms/step
Epoch 306/1000
20/20 - 1s - loss: 0.2641 - categorical_accuracy: 0.9045 - val_loss: 0.2801 - val_categorical_accuracy: 0.8983 - 631ms/epoch - 32ms/step
Epoch 307/1000
20/20 - 1s - loss: 0.2839 - categorical_accuracy: 0.8964 - val_loss: 0.2705 - val_categorical_accuracy: 0.9044 - 663ms/epoch - 33ms/step
Epoch 308/1000
20/20 - 1s - loss: 0.2363 - categorical_accuracy: 0.9175 - val_loss: 0.2988 - val_categorical_accuracy: 0.8916 - 647ms/epoch - 32ms/step
Epoch 309/1000
20/20 - 1s - loss: 0.2823 - categorical_accuracy: 0.8950 - val_loss: 0.3767 - val_categorical_accuracy: 0.8602 - 673ms/epoch - 34ms/step
Epoch 310/1000
20/20 - 1s - loss: 0.2603 - categorical_accuracy: 0.9070 - val_loss: 0.2694 - val_categorical_accuracy: 0.9038 - 729ms/epoch - 36ms/step
Epoch 311/1000
20/20 - 1s - loss: 0.2513 - categorical_accuracy: 0.9102 - val_loss: 0.2922 - val_categorical_accuracy: 0.8952 - 745ms/epoch - 37ms/step
Epoch 312/1000
20/20 - 1s - loss: 0.2700 - categorical_accuracy: 0.9014 - val_loss: 0.3286 - val_categorical_accuracy: 0.8800 - 762ms/epoch - 38ms/step
Epoch 313/1000
20/20 - 1s - loss: 0.2589 - categorical_accuracy: 0.9061 - val_loss: 0.2647 - val_categorical_accuracy: 0.9057 - 682ms/epoch - 34ms/step
Epoch 314/1000
20/20 - 1s - loss: 0.5301 - categorical_accuracy: 0.8370 - val_loss: 0.2941 - val_categorical_accuracy: 0.8978 - 647ms/epoch - 32ms/step
Epoch 315/1000
20/20 - 1s - loss: 0.2425 - categorical_accuracy: 0.9175 - val_loss: 0.2648 - val_categorical_accuracy: 0.9068 - 689ms/epoch - 34ms/step
Epoch 316/1000
20/20 - 1s - loss: 0.2606 - categorical_accuracy: 0.9067 - val_loss: 0.3316 - val_categorical_accuracy: 0.8771 - 732ms/epoch - 37ms/step
Epoch 317/1000
20/20 - 1s - loss: 0.2426 - categorical_accuracy: 0.9141 - val_loss: 0.2821 - val_categorical_accuracy: 0.8970 - 655ms/epoch - 33ms/step
Epoch 318/1000
20/20 - 1s - loss: 0.2712 - categorical_accuracy: 0.8995 - val_loss: 0.2829 - val_categorical_accuracy: 0.8974 - 747ms/epoch - 37ms/step
Epoch 319/1000
20/20 - 1s - loss: 0.2574 - categorical_accuracy: 0.9072 - val_loss: 0.2671 - val_categorical_accuracy: 0.9043 - 736ms/epoch - 37ms/step
Epoch 320/1000
20/20 - 4s - loss: 0.2365 - categorical_accuracy: 0.9164 - val_loss: 0.3004 - val_categorical_accuracy: 0.8902 - 4s/epoch - 203ms/step
Epoch 321/1000
20/20 - 1s - loss: 0.2686 - categorical_accuracy: 0.9009 - val_loss: 0.3996 - val_categorical_accuracy: 0.8543 - 680ms/epoch - 34ms/step
Epoch 322/1000
20/20 - 1s - loss: 0.2474 - categorical_accuracy: 0.9120 - val_loss: 0.3280 - val_categorical_accuracy: 0.8804 - 607ms/epoch - 30ms/step
Epoch 323/1000
20/20 - 1s - loss: 0.2580 - categorical_accuracy: 0.9065 - val_loss: 0.3349 - val_categorical_accuracy: 0.8793 - 569ms/epoch - 28ms/step
Epoch 324/1000
20/20 - 1s - loss: 0.3546 - categorical_accuracy: 0.8861 - val_loss: 0.2504 - val_categorical_accuracy: 0.9120 - 661ms/epoch - 33ms/step
Epoch 325/1000
20/20 - 1s - loss: 0.2319 - categorical_accuracy: 0.9194 - val_loss: 0.3846 - val_categorical_accuracy: 0.8613 - 612ms/epoch - 31ms/step
Epoch 326/1000
20/20 - 1s - loss: 0.2738 - categorical_accuracy: 0.8996 - val_loss: 0.3455 - val_categorical_accuracy: 0.8728 - 628ms/epoch - 31ms/step
Epoch 327/1000
20/20 - 1s - loss: 0.2435 - categorical_accuracy: 0.9118 - val_loss: 0.2862 - val_categorical_accuracy: 0.8959 - 706ms/epoch - 35ms/step
Epoch 328/1000
20/20 - 1s - loss: 0.2490 - categorical_accuracy: 0.9094 - val_loss: 0.2948 - val_categorical_accuracy: 0.8919 - 595ms/epoch - 30ms/step
Epoch 329/1000
20/20 - 1s - loss: 0.2354 - categorical_accuracy: 0.9164 - val_loss: 0.2555 - val_categorical_accuracy: 0.9086 - 610ms/epoch - 30ms/step
Epoch 330/1000
20/20 - 1s - loss: 0.2819 - categorical_accuracy: 0.8960 - val_loss: 0.2597 - val_categorical_accuracy: 0.9087 - 534ms/epoch - 27ms/step
Epoch 331/1000
20/20 - 1s - loss: 0.2334 - categorical_accuracy: 0.9183 - val_loss: 0.3200 - val_categorical_accuracy: 0.8847 - 594ms/epoch - 30ms/step
Epoch 332/1000
20/20 - 1s - loss: 0.2352 - categorical_accuracy: 0.9171 - val_loss: 0.2547 - val_categorical_accuracy: 0.9097 - 643ms/epoch - 32ms/step
Epoch 333/1000
20/20 - 1s - loss: 0.2654 - categorical_accuracy: 0.9021 - val_loss: 0.3570 - val_categorical_accuracy: 0.8670 - 583ms/epoch - 29ms/step
Epoch 334/1000
20/20 - 1s - loss: 0.2485 - categorical_accuracy: 0.9110 - val_loss: 0.2708 - val_categorical_accuracy: 0.9030 - 598ms/epoch - 30ms/step
Epoch 335/1000
20/20 - 1s - loss: 0.2445 - categorical_accuracy: 0.9115 - val_loss: 0.2748 - val_categorical_accuracy: 0.9002 - 623ms/epoch - 31ms/step
Epoch 336/1000
20/20 - 1s - loss: 0.2379 - categorical_accuracy: 0.9137 - val_loss: 0.2595 - val_categorical_accuracy: 0.9081 - 633ms/epoch - 32ms/step
Epoch 337/1000
20/20 - 1s - loss: 0.2600 - categorical_accuracy: 0.9058 - val_loss: 0.2500 - val_categorical_accuracy: 0.9128 - 625ms/epoch - 31ms/step
Epoch 338/1000
20/20 - 1s - loss: 0.2265 - categorical_accuracy: 0.9194 - val_loss: 0.2543 - val_categorical_accuracy: 0.9091 - 671ms/epoch - 34ms/step
Epoch 339/1000
20/20 - 1s - loss: 0.4073 - categorical_accuracy: 0.8701 - val_loss: 0.3047 - val_categorical_accuracy: 0.8944 - 679ms/epoch - 34ms/step
Epoch 340/1000
20/20 - 1s - loss: 0.2270 - categorical_accuracy: 0.9233 - val_loss: 0.2417 - val_categorical_accuracy: 0.9153 - 604ms/epoch - 30ms/step
Epoch 341/1000
20/20 - 1s - loss: 0.2192 - categorical_accuracy: 0.9238 - val_loss: 0.3819 - val_categorical_accuracy: 0.8594 - 611ms/epoch - 31ms/step
Epoch 342/1000
20/20 - 1s - loss: 0.2731 - categorical_accuracy: 0.8989 - val_loss: 0.2935 - val_categorical_accuracy: 0.8914 - 608ms/epoch - 30ms/step
Epoch 343/1000
20/20 - 1s - loss: 0.2379 - categorical_accuracy: 0.9136 - val_loss: 0.2613 - val_categorical_accuracy: 0.9045 - 617ms/epoch - 31ms/step
Epoch 344/1000
20/20 - 1s - loss: 0.2274 - categorical_accuracy: 0.9193 - val_loss: 0.2888 - val_categorical_accuracy: 0.8950 - 659ms/epoch - 33ms/step
Epoch 345/1000
20/20 - 1s - loss: 0.2547 - categorical_accuracy: 0.9073 - val_loss: 0.2407 - val_categorical_accuracy: 0.9159 - 673ms/epoch - 34ms/step
Epoch 346/1000
20/20 - 1s - loss: 0.2226 - categorical_accuracy: 0.9214 - val_loss: 0.2708 - val_categorical_accuracy: 0.9003 - 658ms/epoch - 33ms/step
Epoch 347/1000
20/20 - 1s - loss: 0.3092 - categorical_accuracy: 0.8904 - val_loss: 0.6361 - val_categorical_accuracy: 0.7895 - 676ms/epoch - 34ms/step
Epoch 348/1000
20/20 - 1s - loss: 0.2426 - categorical_accuracy: 0.9177 - val_loss: 0.2384 - val_categorical_accuracy: 0.9171 - 640ms/epoch - 32ms/step
Epoch 349/1000
20/20 - 1s - loss: 0.2501 - categorical_accuracy: 0.9100 - val_loss: 0.3445 - val_categorical_accuracy: 0.8724 - 658ms/epoch - 33ms/step
Epoch 350/1000
20/20 - 1s - loss: 0.2340 - categorical_accuracy: 0.9156 - val_loss: 0.2472 - val_categorical_accuracy: 0.9124 - 599ms/epoch - 30ms/step
Epoch 351/1000
20/20 - 1s - loss: 0.2194 - categorical_accuracy: 0.9217 - val_loss: 0.2619 - val_categorical_accuracy: 0.9044 - 612ms/epoch - 31ms/step
Epoch 352/1000
20/20 - 1s - loss: 0.2445 - categorical_accuracy: 0.9115 - val_loss: 0.3194 - val_categorical_accuracy: 0.8823 - 609ms/epoch - 30ms/step
Epoch 353/1000
20/20 - 1s - loss: 0.2254 - categorical_accuracy: 0.9198 - val_loss: 0.2706 - val_categorical_accuracy: 0.9003 - 660ms/epoch - 33ms/step
Epoch 354/1000
20/20 - 1s - loss: 0.2512 - categorical_accuracy: 0.9077 - val_loss: 0.2502 - val_categorical_accuracy: 0.9115 - 641ms/epoch - 32ms/step
Epoch 355/1000
20/20 - 1s - loss: 0.2193 - categorical_accuracy: 0.9226 - val_loss: 0.2775 - val_categorical_accuracy: 0.9000 - 628ms/epoch - 31ms/step
Epoch 356/1000
20/20 - 1s - loss: 0.2176 - categorical_accuracy: 0.9226 - val_loss: 0.2478 - val_categorical_accuracy: 0.9109 - 656ms/epoch - 33ms/step
Epoch 357/1000
20/20 - 1s - loss: 0.2119 - categorical_accuracy: 0.9249 - val_loss: 0.2662 - val_categorical_accuracy: 0.9041 - 631ms/epoch - 32ms/step
Epoch 358/1000
20/20 - 1s - loss: 0.4000 - categorical_accuracy: 0.8705 - val_loss: 0.2512 - val_categorical_accuracy: 0.9143 - 612ms/epoch - 31ms/step
Epoch 359/1000
20/20 - 1s - loss: 0.2085 - categorical_accuracy: 0.9297 - val_loss: 0.2841 - val_categorical_accuracy: 0.8974 - 657ms/epoch - 33ms/step
Epoch 360/1000
20/20 - 1s - loss: 0.2483 - categorical_accuracy: 0.9113 - val_loss: 0.2303 - val_categorical_accuracy: 0.9202 - 664ms/epoch - 33ms/step
Epoch 361/1000
20/20 - 1s - loss: 0.2116 - categorical_accuracy: 0.9258 - val_loss: 0.2501 - val_categorical_accuracy: 0.9097 - 642ms/epoch - 32ms/step
Epoch 362/1000
20/20 - 1s - loss: 0.2512 - categorical_accuracy: 0.9075 - val_loss: 0.4018 - val_categorical_accuracy: 0.8520 - 674ms/epoch - 34ms/step
Epoch 363/1000
20/20 - 1s - loss: 0.2282 - categorical_accuracy: 0.9214 - val_loss: 0.2604 - val_categorical_accuracy: 0.9085 - 603ms/epoch - 30ms/step
Epoch 364/1000
20/20 - 1s - loss: 0.2063 - categorical_accuracy: 0.9281 - val_loss: 0.3042 - val_categorical_accuracy: 0.8899 - 742ms/epoch - 37ms/step
Epoch 365/1000
20/20 - 1s - loss: 0.2343 - categorical_accuracy: 0.9147 - val_loss: 0.2344 - val_categorical_accuracy: 0.9175 - 619ms/epoch - 31ms/step
Epoch 366/1000
20/20 - 1s - loss: 0.2536 - categorical_accuracy: 0.9081 - val_loss: 0.2311 - val_categorical_accuracy: 0.9193 - 595ms/epoch - 30ms/step
Epoch 367/1000
20/20 - 1s - loss: 0.2229 - categorical_accuracy: 0.9205 - val_loss: 0.2380 - val_categorical_accuracy: 0.9151 - 732ms/epoch - 37ms/step
Epoch 368/1000
20/20 - 1s - loss: 0.2048 - categorical_accuracy: 0.9283 - val_loss: 0.2768 - val_categorical_accuracy: 0.9007 - 716ms/epoch - 36ms/step
Epoch 369/1000
20/20 - 1s - loss: 0.2326 - categorical_accuracy: 0.9150 - val_loss: 0.2339 - val_categorical_accuracy: 0.9177 - 720ms/epoch - 36ms/step
Epoch 370/1000
20/20 - 1s - loss: 0.2473 - categorical_accuracy: 0.9104 - val_loss: 0.2491 - val_categorical_accuracy: 0.9104 - 693ms/epoch - 35ms/step
Epoch 371/1000
20/20 - 1s - loss: 0.2050 - categorical_accuracy: 0.9281 - val_loss: 0.2373 - val_categorical_accuracy: 0.9174 - 633ms/epoch - 32ms/step
Epoch 372/1000
20/20 - 1s - loss: 0.2081 - categorical_accuracy: 0.9272 - val_loss: 0.2431 - val_categorical_accuracy: 0.9156 - 611ms/epoch - 31ms/step
Epoch 373/1000
20/20 - 1s - loss: 0.4240 - categorical_accuracy: 0.8724 - val_loss: 0.2401 - val_categorical_accuracy: 0.9177 - 664ms/epoch - 33ms/step
Epoch 374/1000
20/20 - 1s - loss: 0.2046 - categorical_accuracy: 0.9306 - val_loss: 0.2773 - val_categorical_accuracy: 0.8998 - 719ms/epoch - 36ms/step
Epoch 375/1000
20/20 - 1s - loss: 0.2387 - categorical_accuracy: 0.9145 - val_loss: 0.2236 - val_categorical_accuracy: 0.9221 - 671ms/epoch - 34ms/step
Epoch 376/1000
20/20 - 1s - loss: 0.2282 - categorical_accuracy: 0.9169 - val_loss: 0.2351 - val_categorical_accuracy: 0.9161 - 719ms/epoch - 36ms/step
Epoch 377/1000
20/20 - 1s - loss: 0.2072 - categorical_accuracy: 0.9275 - val_loss: 0.4134 - val_categorical_accuracy: 0.8554 - 689ms/epoch - 34ms/step
Epoch 378/1000
20/20 - 1s - loss: 0.2358 - categorical_accuracy: 0.9148 - val_loss: 0.2454 - val_categorical_accuracy: 0.9117 - 671ms/epoch - 34ms/step
Epoch 379/1000
20/20 - 1s - loss: 0.2193 - categorical_accuracy: 0.9207 - val_loss: 0.2385 - val_categorical_accuracy: 0.9163 - 646ms/epoch - 32ms/step
Epoch 380/1000
20/20 - 1s - loss: 0.2343 - categorical_accuracy: 0.9151 - val_loss: 0.2248 - val_categorical_accuracy: 0.9217 - 647ms/epoch - 32ms/step
Epoch 381/1000
20/20 - 1s - loss: 0.2067 - categorical_accuracy: 0.9268 - val_loss: 0.2802 - val_categorical_accuracy: 0.8991 - 619ms/epoch - 31ms/step
Epoch 382/1000
20/20 - 1s - loss: 0.2309 - categorical_accuracy: 0.9165 - val_loss: 0.2417 - val_categorical_accuracy: 0.9146 - 593ms/epoch - 30ms/step
Epoch 383/1000
20/20 - 1s - loss: 0.2229 - categorical_accuracy: 0.9203 - val_loss: 0.3401 - val_categorical_accuracy: 0.8757 - 650ms/epoch - 32ms/step
Epoch 384/1000
20/20 - 1s - loss: 0.2145 - categorical_accuracy: 0.9238 - val_loss: 0.2543 - val_categorical_accuracy: 0.9095 - 703ms/epoch - 35ms/step
Epoch 385/1000
20/20 - 1s - loss: 0.2290 - categorical_accuracy: 0.9166 - val_loss: 0.3631 - val_categorical_accuracy: 0.8648 - 651ms/epoch - 33ms/step
Epoch 386/1000
20/20 - 1s - loss: 0.2176 - categorical_accuracy: 0.9220 - val_loss: 0.2178 - val_categorical_accuracy: 0.9242 - 676ms/epoch - 34ms/step
Epoch 387/1000
20/20 - 1s - loss: 0.1789 - categorical_accuracy: 0.9397 - val_loss: 0.2260 - val_categorical_accuracy: 0.9211 - 718ms/epoch - 36ms/step
Epoch 388/1000
20/20 - 1s - loss: 0.9440 - categorical_accuracy: 0.8383 - val_loss: 6.3026 - val_categorical_accuracy: 0.2056 - 677ms/epoch - 34ms/step
Epoch 389/1000
20/20 - 1s - loss: 1.6449 - categorical_accuracy: 0.5267 - val_loss: 0.7619 - val_categorical_accuracy: 0.7330 - 695ms/epoch - 35ms/step
Epoch 390/1000
20/20 - 1s - loss: 0.5831 - categorical_accuracy: 0.7950 - val_loss: 0.4572 - val_categorical_accuracy: 0.8398 - 655ms/epoch - 33ms/step
Epoch 391/1000
20/20 - 2s - loss: 0.3842 - categorical_accuracy: 0.8675 - val_loss: 0.3507 - val_categorical_accuracy: 0.8778 - 2s/epoch - 99ms/step
Epoch 392/1000
20/20 - 1s - loss: 0.2967 - categorical_accuracy: 0.8988 - val_loss: 0.3034 - val_categorical_accuracy: 0.8938 - 608ms/epoch - 30ms/step
Epoch 393/1000
20/20 - 1s - loss: 0.2802 - categorical_accuracy: 0.9011 - val_loss: 0.3130 - val_categorical_accuracy: 0.8887 - 580ms/epoch - 29ms/step
Epoch 394/1000
20/20 - 1s - loss: 0.2449 - categorical_accuracy: 0.9153 - val_loss: 0.2623 - val_categorical_accuracy: 0.9085 - 594ms/epoch - 30ms/step
Epoch 395/1000
20/20 - 1s - loss: 0.2474 - categorical_accuracy: 0.9129 - val_loss: 0.2624 - val_categorical_accuracy: 0.9077 - 589ms/epoch - 29ms/step
Epoch 396/1000
20/20 - 1s - loss: 0.2188 - categorical_accuracy: 0.9251 - val_loss: 0.2433 - val_categorical_accuracy: 0.9150 - 585ms/epoch - 29ms/step
Epoch 397/1000
20/20 - 1s - loss: 0.2456 - categorical_accuracy: 0.9119 - val_loss: 0.2595 - val_categorical_accuracy: 0.9080 - 580ms/epoch - 29ms/step
Epoch 398/1000
20/20 - 1s - loss: 0.2331 - categorical_accuracy: 0.9174 - val_loss: 0.2643 - val_categorical_accuracy: 0.9057 - 568ms/epoch - 28ms/step
Epoch 399/1000
20/20 - 1s - loss: 0.2161 - categorical_accuracy: 0.9245 - val_loss: 0.2528 - val_categorical_accuracy: 0.9107 - 534ms/epoch - 27ms/step
Epoch 400/1000
20/20 - 1s - loss: 0.2287 - categorical_accuracy: 0.9179 - val_loss: 0.2568 - val_categorical_accuracy: 0.9081 - 564ms/epoch - 28ms/step
Epoch 401/1000
20/20 - 1s - loss: 0.2319 - categorical_accuracy: 0.9168 - val_loss: 0.2646 - val_categorical_accuracy: 0.9062 - 667ms/epoch - 33ms/step
Epoch 402/1000
20/20 - 1s - loss: 0.2198 - categorical_accuracy: 0.9218 - val_loss: 0.2610 - val_categorical_accuracy: 0.9076 - 989ms/epoch - 49ms/step
Epoch 403/1000
20/20 - 1s - loss: 0.2067 - categorical_accuracy: 0.9273 - val_loss: 0.2426 - val_categorical_accuracy: 0.9147 - 601ms/epoch - 30ms/step
Epoch 404/1000
20/20 - 1s - loss: 0.2326 - categorical_accuracy: 0.9156 - val_loss: 0.2837 - val_categorical_accuracy: 0.8986 - 641ms/epoch - 32ms/step
Epoch 405/1000
20/20 - 1s - loss: 0.2160 - categorical_accuracy: 0.9234 - val_loss: 0.2433 - val_categorical_accuracy: 0.9151 - 596ms/epoch - 30ms/step
Epoch 406/1000
20/20 - 1s - loss: 0.2065 - categorical_accuracy: 0.9294 - val_loss: 0.2264 - val_categorical_accuracy: 0.9215 - 554ms/epoch - 28ms/step
Epoch 407/1000
20/20 - 1s - loss: 0.2279 - categorical_accuracy: 0.9182 - val_loss: 0.2537 - val_categorical_accuracy: 0.9107 - 556ms/epoch - 28ms/step
Epoch 408/1000
20/20 - 1s - loss: 0.1971 - categorical_accuracy: 0.9318 - val_loss: 0.2812 - val_categorical_accuracy: 0.8968 - 567ms/epoch - 28ms/step
Epoch 409/1000
20/20 - 1s - loss: 0.2289 - categorical_accuracy: 0.9176 - val_loss: 0.2259 - val_categorical_accuracy: 0.9222 - 694ms/epoch - 35ms/step
Epoch 410/1000
20/20 - 1s - loss: 0.2035 - categorical_accuracy: 0.9298 - val_loss: 0.2434 - val_categorical_accuracy: 0.9159 - 1s/epoch - 54ms/step
Epoch 411/1000
20/20 - 2s - loss: 0.2226 - categorical_accuracy: 0.9197 - val_loss: 0.2505 - val_categorical_accuracy: 0.9105 - 2s/epoch - 124ms/step
Epoch 412/1000
20/20 - 1s - loss: 0.2074 - categorical_accuracy: 0.9264 - val_loss: 0.3010 - val_categorical_accuracy: 0.8916 - 783ms/epoch - 39ms/step
Epoch 413/1000
20/20 - 1s - loss: 0.2081 - categorical_accuracy: 0.9256 - val_loss: 0.2358 - val_categorical_accuracy: 0.9170 - 799ms/epoch - 40ms/step
Epoch 414/1000
20/20 - 1s - loss: 0.2033 - categorical_accuracy: 0.9300 - val_loss: 0.2239 - val_categorical_accuracy: 0.9218 - 867ms/epoch - 43ms/step
Epoch 415/1000
20/20 - 2s - loss: 0.2340 - categorical_accuracy: 0.9158 - val_loss: 0.2148 - val_categorical_accuracy: 0.9255 - 2s/epoch - 123ms/step
Epoch 416/1000
20/20 - 1s - loss: 0.1832 - categorical_accuracy: 0.9380 - val_loss: 0.2445 - val_categorical_accuracy: 0.9132 - 1s/epoch - 70ms/step
Epoch 417/1000
20/20 - 1s - loss: 0.2081 - categorical_accuracy: 0.9250 - val_loss: 0.2399 - val_categorical_accuracy: 0.9135 - 1s/epoch - 65ms/step
Epoch 418/1000
20/20 - 1s - loss: 0.2156 - categorical_accuracy: 0.9225 - val_loss: 0.3089 - val_categorical_accuracy: 0.8895 - 632ms/epoch - 32ms/step
Epoch 419/1000
20/20 - 1s - loss: 0.1973 - categorical_accuracy: 0.9312 - val_loss: 0.2434 - val_categorical_accuracy: 0.9143 - 673ms/epoch - 34ms/step
Epoch 420/1000
20/20 - 3s - loss: 0.2008 - categorical_accuracy: 0.9284 - val_loss: 0.2349 - val_categorical_accuracy: 0.9169 - 3s/epoch - 168ms/step
Epoch 421/1000
20/20 - 1s - loss: 0.2034 - categorical_accuracy: 0.9277 - val_loss: 0.2551 - val_categorical_accuracy: 0.9102 - 783ms/epoch - 39ms/step
Epoch 422/1000
20/20 - 1s - loss: 0.1920 - categorical_accuracy: 0.9321 - val_loss: 0.2389 - val_categorical_accuracy: 0.9150 - 782ms/epoch - 39ms/step
Epoch 423/1000
20/20 - 1s - loss: 0.2581 - categorical_accuracy: 0.9066 - val_loss: 0.2232 - val_categorical_accuracy: 0.9224 - 750ms/epoch - 37ms/step
Epoch 424/1000
20/20 - 6s - loss: 0.1846 - categorical_accuracy: 0.9370 - val_loss: 0.2384 - val_categorical_accuracy: 0.9166 - 6s/epoch - 301ms/step
Epoch 425/1000
20/20 - 1s - loss: 0.1923 - categorical_accuracy: 0.9321 - val_loss: 0.2486 - val_categorical_accuracy: 0.9120 - 934ms/epoch - 47ms/step
Epoch 426/1000
20/20 - 1s - loss: 0.1994 - categorical_accuracy: 0.9292 - val_loss: 0.2679 - val_categorical_accuracy: 0.9052 - 1s/epoch - 52ms/step
Epoch 427/1000
20/20 - 1s - loss: 0.2367 - categorical_accuracy: 0.9144 - val_loss: 0.3125 - val_categorical_accuracy: 0.8938 - 1s/epoch - 66ms/step
Epoch 428/1000
20/20 - 2s - loss: 0.1903 - categorical_accuracy: 0.9349 - val_loss: 0.2306 - val_categorical_accuracy: 0.9189 - 2s/epoch - 78ms/step
Epoch 429/1000
20/20 - 1s - loss: 0.2189 - categorical_accuracy: 0.9211 - val_loss: 0.2289 - val_categorical_accuracy: 0.9205 - 1s/epoch - 62ms/step
Epoch 430/1000
20/20 - 1s - loss: 0.1810 - categorical_accuracy: 0.9377 - val_loss: 0.2377 - val_categorical_accuracy: 0.9145 - 1s/epoch - 61ms/step
Epoch 431/1000
20/20 - 2s - loss: 0.1961 - categorical_accuracy: 0.9300 - val_loss: 0.2147 - val_categorical_accuracy: 0.9257 - 2s/epoch - 95ms/step
Epoch 432/1000
20/20 - 3s - loss: 0.1958 - categorical_accuracy: 0.9317 - val_loss: 0.2155 - val_categorical_accuracy: 0.9256 - 3s/epoch - 143ms/step
Epoch 433/1000
20/20 - 2s - loss: 0.2455 - categorical_accuracy: 0.9116 - val_loss: 0.2324 - val_categorical_accuracy: 0.9182 - 2s/epoch - 108ms/step
Epoch 434/1000
20/20 - 2s - loss: 0.1857 - categorical_accuracy: 0.9353 - val_loss: 0.2282 - val_categorical_accuracy: 0.9198 - 2s/epoch - 82ms/step
Epoch 435/1000
20/20 - 1s - loss: 0.1905 - categorical_accuracy: 0.9321 - val_loss: 0.2364 - val_categorical_accuracy: 0.9171 - 964ms/epoch - 48ms/step
Epoch 436/1000
20/20 - 1s - loss: 0.1839 - categorical_accuracy: 0.9364 - val_loss: 0.2186 - val_categorical_accuracy: 0.9245 - 1s/epoch - 57ms/step
Epoch 437/1000
20/20 - 1s - loss: 0.2187 - categorical_accuracy: 0.9209 - val_loss: 0.2270 - val_categorical_accuracy: 0.9201 - 1s/epoch - 53ms/step
Epoch 438/1000
20/20 - 1s - loss: 0.1706 - categorical_accuracy: 0.9422 - val_loss: 0.2053 - val_categorical_accuracy: 0.9285 - 1s/epoch - 61ms/step
Epoch 439/1000
20/20 - 4s - loss: 0.2121 - categorical_accuracy: 0.9221 - val_loss: 0.3284 - val_categorical_accuracy: 0.8820 - 4s/epoch - 177ms/step
Epoch 440/1000
20/20 - 3s - loss: 0.2090 - categorical_accuracy: 0.9255 - val_loss: 0.2225 - val_categorical_accuracy: 0.9233 - 3s/epoch - 140ms/step
Epoch 441/1000
20/20 - 3s - loss: 0.1760 - categorical_accuracy: 0.9404 - val_loss: 0.2163 - val_categorical_accuracy: 0.9241 - 3s/epoch - 133ms/step
Epoch 442/1000
20/20 - 1s - loss: 0.2281 - categorical_accuracy: 0.9163 - val_loss: 0.3128 - val_categorical_accuracy: 0.8890 - 736ms/epoch - 37ms/step
Epoch 443/1000
20/20 - 0s - loss: 0.1758 - categorical_accuracy: 0.9395 - val_loss: 0.2044 - val_categorical_accuracy: 0.9290 - 476ms/epoch - 24ms/step
Epoch 444/1000
20/20 - 0s - loss: 1.3888 - categorical_accuracy: 0.7160 - val_loss: 1.2460 - val_categorical_accuracy: 0.5967 - 430ms/epoch - 22ms/step
Epoch 445/1000
20/20 - 0s - loss: 0.7704 - categorical_accuracy: 0.7420 - val_loss: 0.4969 - val_categorical_accuracy: 0.8252 - 443ms/epoch - 22ms/step
Epoch 446/1000
20/20 - 0s - loss: 0.3937 - categorical_accuracy: 0.8600 - val_loss: 0.3413 - val_categorical_accuracy: 0.8792 - 438ms/epoch - 22ms/step
Epoch 447/1000
20/20 - 1s - loss: 0.2845 - categorical_accuracy: 0.9012 - val_loss: 0.2825 - val_categorical_accuracy: 0.9019 - 503ms/epoch - 25ms/step
Epoch 448/1000
20/20 - 0s - loss: 0.2357 - categorical_accuracy: 0.9190 - val_loss: 0.2559 - val_categorical_accuracy: 0.9113 - 445ms/epoch - 22ms/step
Epoch 449/1000
20/20 - 1s - loss: 0.2133 - categorical_accuracy: 0.9269 - val_loss: 0.2546 - val_categorical_accuracy: 0.9107 - 520ms/epoch - 26ms/step
Epoch 450/1000
20/20 - 0s - loss: 0.2256 - categorical_accuracy: 0.9198 - val_loss: 0.2356 - val_categorical_accuracy: 0.9183 - 498ms/epoch - 25ms/step
Epoch 451/1000
20/20 - 0s - loss: 0.1881 - categorical_accuracy: 0.9357 - val_loss: 0.2282 - val_categorical_accuracy: 0.9204 - 486ms/epoch - 24ms/step
Epoch 452/1000
20/20 - 0s - loss: 0.2379 - categorical_accuracy: 0.9151 - val_loss: 0.2296 - val_categorical_accuracy: 0.9197 - 459ms/epoch - 23ms/step
Epoch 453/1000
20/20 - 0s - loss: 0.1775 - categorical_accuracy: 0.9404 - val_loss: 0.2153 - val_categorical_accuracy: 0.9259 - 474ms/epoch - 24ms/step
Epoch 454/1000
20/20 - 0s - loss: 0.1738 - categorical_accuracy: 0.9418 - val_loss: 0.2210 - val_categorical_accuracy: 0.9236 - 482ms/epoch - 24ms/step
Epoch 455/1000
20/20 - 0s - loss: 0.1894 - categorical_accuracy: 0.9345 - val_loss: 0.2606 - val_categorical_accuracy: 0.9068 - 472ms/epoch - 24ms/step
Epoch 456/1000
20/20 - 0s - loss: 0.1984 - categorical_accuracy: 0.9290 - val_loss: 0.2724 - val_categorical_accuracy: 0.9041 - 472ms/epoch - 24ms/step
Epoch 457/1000
20/20 - 1s - loss: 0.2014 - categorical_accuracy: 0.9292 - val_loss: 0.2120 - val_categorical_accuracy: 0.9267 - 515ms/epoch - 26ms/step
Epoch 458/1000
20/20 - 1s - loss: 0.1669 - categorical_accuracy: 0.9443 - val_loss: 0.2132 - val_categorical_accuracy: 0.9266 - 550ms/epoch - 27ms/step
Epoch 459/1000
20/20 - 1s - loss: 0.2006 - categorical_accuracy: 0.9285 - val_loss: 0.2274 - val_categorical_accuracy: 0.9203 - 504ms/epoch - 25ms/step
Epoch 460/1000
20/20 - 1s - loss: 0.1914 - categorical_accuracy: 0.9319 - val_loss: 0.3014 - val_categorical_accuracy: 0.8932 - 521ms/epoch - 26ms/step
Epoch 461/1000
20/20 - 1s - loss: 0.1862 - categorical_accuracy: 0.9348 - val_loss: 0.2013 - val_categorical_accuracy: 0.9302 - 505ms/epoch - 25ms/step
Epoch 462/1000
20/20 - 1s - loss: 0.1673 - categorical_accuracy: 0.9430 - val_loss: 0.2185 - val_categorical_accuracy: 0.9248 - 547ms/epoch - 27ms/step
Epoch 463/1000
20/20 - 1s - loss: 0.2067 - categorical_accuracy: 0.9255 - val_loss: 0.2491 - val_categorical_accuracy: 0.9124 - 505ms/epoch - 25ms/step
Epoch 464/1000
20/20 - 1s - loss: 0.1716 - categorical_accuracy: 0.9415 - val_loss: 0.2237 - val_categorical_accuracy: 0.9226 - 521ms/epoch - 26ms/step
Epoch 465/1000
20/20 - 1s - loss: 0.2313 - categorical_accuracy: 0.9183 - val_loss: 0.2032 - val_categorical_accuracy: 0.9303 - 508ms/epoch - 25ms/step
Epoch 466/1000
20/20 - 0s - loss: 0.1705 - categorical_accuracy: 0.9416 - val_loss: 0.2238 - val_categorical_accuracy: 0.9230 - 494ms/epoch - 25ms/step
Epoch 467/1000
20/20 - 1s - loss: 0.1892 - categorical_accuracy: 0.9325 - val_loss: 0.2164 - val_categorical_accuracy: 0.9251 - 504ms/epoch - 25ms/step
Epoch 468/1000
20/20 - 1s - loss: 0.1845 - categorical_accuracy: 0.9338 - val_loss: 0.2213 - val_categorical_accuracy: 0.9227 - 515ms/epoch - 26ms/step
Epoch 469/1000
20/20 - 0s - loss: 0.1641 - categorical_accuracy: 0.9441 - val_loss: 0.2085 - val_categorical_accuracy: 0.9279 - 486ms/epoch - 24ms/step
Epoch 470/1000
20/20 - 1s - loss: 0.2008 - categorical_accuracy: 0.9277 - val_loss: 0.2132 - val_categorical_accuracy: 0.9265 - 514ms/epoch - 26ms/step
Epoch 471/1000
20/20 - 1s - loss: 0.4091 - categorical_accuracy: 0.8943 - val_loss: 1.2766 - val_categorical_accuracy: 0.6946 - 517ms/epoch - 26ms/step
Epoch 472/1000
20/20 - 0s - loss: 0.3680 - categorical_accuracy: 0.8885 - val_loss: 0.2186 - val_categorical_accuracy: 0.9245 - 441ms/epoch - 22ms/step
Epoch 473/1000
20/20 - 0s - loss: 0.1686 - categorical_accuracy: 0.9441 - val_loss: 0.2061 - val_categorical_accuracy: 0.9293 - 454ms/epoch - 23ms/step
Epoch 474/1000
20/20 - 0s - loss: 0.1612 - categorical_accuracy: 0.9466 - val_loss: 0.2317 - val_categorical_accuracy: 0.9199 - 435ms/epoch - 22ms/step
Epoch 475/1000
20/20 - 0s - loss: 0.2240 - categorical_accuracy: 0.9173 - val_loss: 0.2125 - val_categorical_accuracy: 0.9258 - 472ms/epoch - 24ms/step
Epoch 476/1000
20/20 - 0s - loss: 0.1630 - categorical_accuracy: 0.9450 - val_loss: 0.2253 - val_categorical_accuracy: 0.9209 - 445ms/epoch - 22ms/step
Epoch 477/1000
20/20 - 0s - loss: 0.1852 - categorical_accuracy: 0.9342 - val_loss: 0.2287 - val_categorical_accuracy: 0.9191 - 436ms/epoch - 22ms/step
Epoch 478/1000
20/20 - 0s - loss: 0.1682 - categorical_accuracy: 0.9423 - val_loss: 0.2162 - val_categorical_accuracy: 0.9241 - 442ms/epoch - 22ms/step
Epoch 479/1000
20/20 - 0s - loss: 0.1726 - categorical_accuracy: 0.9397 - val_loss: 0.2046 - val_categorical_accuracy: 0.9298 - 443ms/epoch - 22ms/step
Epoch 480/1000
20/20 - 0s - loss: 0.1629 - categorical_accuracy: 0.9442 - val_loss: 0.2699 - val_categorical_accuracy: 0.9062 - 474ms/epoch - 24ms/step
Epoch 481/1000
20/20 - 17s - loss: 0.2270 - categorical_accuracy: 0.9205 - val_loss: 0.1997 - val_categorical_accuracy: 0.9320 - 17s/epoch - 864ms/step
Epoch 482/1000
20/20 - 1s - loss: 0.1790 - categorical_accuracy: 0.9369 - val_loss: 0.2241 - val_categorical_accuracy: 0.9206 - 513ms/epoch - 26ms/step
Epoch 483/1000
20/20 - 1s - loss: 0.1688 - categorical_accuracy: 0.9413 - val_loss: 0.2028 - val_categorical_accuracy: 0.9307 - 519ms/epoch - 26ms/step
Epoch 484/1000
20/20 - 1s - loss: 0.1629 - categorical_accuracy: 0.9445 - val_loss: 0.2382 - val_categorical_accuracy: 0.9156 - 559ms/epoch - 28ms/step
Epoch 485/1000
20/20 - 1s - loss: 0.1931 - categorical_accuracy: 0.9318 - val_loss: 0.2103 - val_categorical_accuracy: 0.9271 - 571ms/epoch - 29ms/step
Epoch 486/1000
20/20 - 10s - loss: 0.2050 - categorical_accuracy: 0.9272 - val_loss: 0.4656 - val_categorical_accuracy: 0.8481 - 10s/epoch - 509ms/step
Epoch 487/1000
20/20 - 1s - loss: 0.1726 - categorical_accuracy: 0.9419 - val_loss: 0.1959 - val_categorical_accuracy: 0.9333 - 513ms/epoch - 26ms/step
Epoch 488/1000
20/20 - 1s - loss: 0.1507 - categorical_accuracy: 0.9501 - val_loss: 0.1962 - val_categorical_accuracy: 0.9325 - 530ms/epoch - 27ms/step
Epoch 489/1000
20/20 - 1s - loss: 0.1650 - categorical_accuracy: 0.9434 - val_loss: 0.2155 - val_categorical_accuracy: 0.9245 - 559ms/epoch - 28ms/step
Epoch 490/1000
20/20 - 1s - loss: 0.2274 - categorical_accuracy: 0.9179 - val_loss: 0.1971 - val_categorical_accuracy: 0.9328 - 527ms/epoch - 26ms/step
Epoch 491/1000
20/20 - 1s - loss: 0.1465 - categorical_accuracy: 0.9514 - val_loss: 0.2141 - val_categorical_accuracy: 0.9261 - 522ms/epoch - 26ms/step
Epoch 492/1000
20/20 - 1s - loss: 0.2151 - categorical_accuracy: 0.9233 - val_loss: 0.1909 - val_categorical_accuracy: 0.9349 - 559ms/epoch - 28ms/step
Epoch 493/1000
20/20 - 1s - loss: 0.1754 - categorical_accuracy: 0.9407 - val_loss: 0.7132 - val_categorical_accuracy: 0.8171 - 502ms/epoch - 25ms/step
Epoch 494/1000
20/20 - 0s - loss: 0.5864 - categorical_accuracy: 0.8422 - val_loss: 0.2224 - val_categorical_accuracy: 0.9238 - 441ms/epoch - 22ms/step
Epoch 495/1000
20/20 - 0s - loss: 0.1698 - categorical_accuracy: 0.9442 - val_loss: 0.2189 - val_categorical_accuracy: 0.9251 - 449ms/epoch - 22ms/step
Epoch 496/1000
20/20 - 0s - loss: 0.1573 - categorical_accuracy: 0.9478 - val_loss: 0.2003 - val_categorical_accuracy: 0.9312 - 448ms/epoch - 22ms/step
Epoch 497/1000
20/20 - 0s - loss: 0.1691 - categorical_accuracy: 0.9412 - val_loss: 0.2507 - val_categorical_accuracy: 0.9122 - 497ms/epoch - 25ms/step
Epoch 498/1000
20/20 - 1s - loss: 0.1746 - categorical_accuracy: 0.9386 - val_loss: 0.1915 - val_categorical_accuracy: 0.9352 - 520ms/epoch - 26ms/step
Epoch 499/1000
20/20 - 1s - loss: 0.1986 - categorical_accuracy: 0.9301 - val_loss: 0.2023 - val_categorical_accuracy: 0.9304 - 543ms/epoch - 27ms/step
Epoch 500/1000
20/20 - 1s - loss: 0.1480 - categorical_accuracy: 0.9510 - val_loss: 0.2061 - val_categorical_accuracy: 0.9298 - 521ms/epoch - 26ms/step
Epoch 501/1000
20/20 - 1s - loss: 0.1799 - categorical_accuracy: 0.9356 - val_loss: 0.2164 - val_categorical_accuracy: 0.9247 - 504ms/epoch - 25ms/step
Epoch 502/1000
20/20 - 1s - loss: 0.1464 - categorical_accuracy: 0.9517 - val_loss: 0.1956 - val_categorical_accuracy: 0.9337 - 526ms/epoch - 26ms/step
Epoch 503/1000
20/20 - 1s - loss: 0.1786 - categorical_accuracy: 0.9366 - val_loss: 0.3737 - val_categorical_accuracy: 0.8726 - 547ms/epoch - 27ms/step
Epoch 504/1000
20/20 - 1s - loss: 0.2042 - categorical_accuracy: 0.9283 - val_loss: 0.2249 - val_categorical_accuracy: 0.9227 - 504ms/epoch - 25ms/step
Epoch 505/1000
20/20 - 1s - loss: 0.1606 - categorical_accuracy: 0.9447 - val_loss: 0.1949 - val_categorical_accuracy: 0.9330 - 515ms/epoch - 26ms/step
Epoch 506/1000
20/20 - 1s - loss: 0.1609 - categorical_accuracy: 0.9447 - val_loss: 0.1955 - val_categorical_accuracy: 0.9334 - 514ms/epoch - 26ms/step
Epoch 507/1000
20/20 - 1s - loss: 0.1548 - categorical_accuracy: 0.9473 - val_loss: 0.2281 - val_categorical_accuracy: 0.9219 - 525ms/epoch - 26ms/step
Epoch 508/1000
20/20 - 1s - loss: 0.2246 - categorical_accuracy: 0.9185 - val_loss: 0.2005 - val_categorical_accuracy: 0.9303 - 515ms/epoch - 26ms/step
Epoch 509/1000
20/20 - 0s - loss: 0.1487 - categorical_accuracy: 0.9498 - val_loss: 0.2100 - val_categorical_accuracy: 0.9264 - 425ms/epoch - 21ms/step
Epoch 510/1000
20/20 - 0s - loss: 0.1771 - categorical_accuracy: 0.9363 - val_loss: 0.1956 - val_categorical_accuracy: 0.9336 - 473ms/epoch - 24ms/step
Epoch 511/1000
20/20 - 0s - loss: 0.1506 - categorical_accuracy: 0.9496 - val_loss: 0.1947 - val_categorical_accuracy: 0.9334 - 481ms/epoch - 24ms/step
Epoch 512/1000
20/20 - 0s - loss: 0.1519 - categorical_accuracy: 0.9483 - val_loss: 0.2025 - val_categorical_accuracy: 0.9306 - 493ms/epoch - 25ms/step
Epoch 513/1000
20/20 - 0s - loss: 0.1880 - categorical_accuracy: 0.9327 - val_loss: 0.1961 - val_categorical_accuracy: 0.9331 - 453ms/epoch - 23ms/step
Epoch 514/1000
20/20 - 1s - loss: 0.1504 - categorical_accuracy: 0.9495 - val_loss: 0.2026 - val_categorical_accuracy: 0.9301 - 1s/epoch - 57ms/step
Epoch 515/1000
20/20 - 6s - loss: 0.2614 - categorical_accuracy: 0.9067 - val_loss: 0.4819 - val_categorical_accuracy: 0.8475 - 6s/epoch - 319ms/step
Epoch 516/1000
20/20 - 1s - loss: 0.2242 - categorical_accuracy: 0.9278 - val_loss: 0.1938 - val_categorical_accuracy: 0.9342 - 736ms/epoch - 37ms/step
Epoch 517/1000
20/20 - 0s - loss: 0.1441 - categorical_accuracy: 0.9523 - val_loss: 0.1935 - val_categorical_accuracy: 0.9337 - 496ms/epoch - 25ms/step
Epoch 518/1000
20/20 - 0s - loss: 0.1702 - categorical_accuracy: 0.9393 - val_loss: 0.2007 - val_categorical_accuracy: 0.9316 - 349ms/epoch - 17ms/step
Epoch 519/1000
20/20 - 0s - loss: 0.1411 - categorical_accuracy: 0.9529 - val_loss: 0.2070 - val_categorical_accuracy: 0.9269 - 344ms/epoch - 17ms/step
Epoch 520/1000
20/20 - 0s - loss: 0.2049 - categorical_accuracy: 0.9266 - val_loss: 0.1991 - val_categorical_accuracy: 0.9322 - 335ms/epoch - 17ms/step
Epoch 521/1000
20/20 - 0s - loss: 0.1819 - categorical_accuracy: 0.9353 - val_loss: 0.1898 - val_categorical_accuracy: 0.9357 - 331ms/epoch - 17ms/step
Epoch 522/1000
20/20 - 0s - loss: 0.1360 - categorical_accuracy: 0.9553 - val_loss: 0.1842 - val_categorical_accuracy: 0.9375 - 334ms/epoch - 17ms/step
Epoch 523/1000
20/20 - 0s - loss: 0.1561 - categorical_accuracy: 0.9460 - val_loss: 0.2296 - val_categorical_accuracy: 0.9178 - 324ms/epoch - 16ms/step
Epoch 524/1000
20/20 - 0s - loss: 0.1507 - categorical_accuracy: 0.9482 - val_loss: 0.1926 - val_categorical_accuracy: 0.9340 - 338ms/epoch - 17ms/step
Epoch 525/1000
20/20 - 0s - loss: 0.1681 - categorical_accuracy: 0.9411 - val_loss: 0.4142 - val_categorical_accuracy: 0.8640 - 358ms/epoch - 18ms/step
Epoch 526/1000
20/20 - 0s - loss: 0.1856 - categorical_accuracy: 0.9367 - val_loss: 0.1829 - val_categorical_accuracy: 0.9383 - 355ms/epoch - 18ms/step
Epoch 527/1000
20/20 - 0s - loss: 0.1407 - categorical_accuracy: 0.9527 - val_loss: 0.1985 - val_categorical_accuracy: 0.9321 - 368ms/epoch - 18ms/step
Epoch 528/1000
20/20 - 0s - loss: 0.2101 - categorical_accuracy: 0.9237 - val_loss: 0.1851 - val_categorical_accuracy: 0.9371 - 353ms/epoch - 18ms/step
Epoch 529/1000
20/20 - 0s - loss: 0.1377 - categorical_accuracy: 0.9544 - val_loss: 0.1851 - val_categorical_accuracy: 0.9375 - 361ms/epoch - 18ms/step
Epoch 530/1000
20/20 - 0s - loss: 0.1505 - categorical_accuracy: 0.9489 - val_loss: 0.1961 - val_categorical_accuracy: 0.9326 - 365ms/epoch - 18ms/step
Epoch 531/1000
20/20 - 0s - loss: 0.1715 - categorical_accuracy: 0.9390 - val_loss: 0.1941 - val_categorical_accuracy: 0.9337 - 349ms/epoch - 17ms/step
Epoch 532/1000
20/20 - 0s - loss: 0.1545 - categorical_accuracy: 0.9471 - val_loss: 0.1953 - val_categorical_accuracy: 0.9333 - 382ms/epoch - 19ms/step
Epoch 533/1000
20/20 - 0s - loss: 0.1421 - categorical_accuracy: 0.9519 - val_loss: 0.1891 - val_categorical_accuracy: 0.9361 - 365ms/epoch - 18ms/step
Epoch 534/1000
20/20 - 0s - loss: 0.1690 - categorical_accuracy: 0.9414 - val_loss: 0.9613 - val_categorical_accuracy: 0.8056 - 358ms/epoch - 18ms/step
Epoch 535/1000
20/20 - 0s - loss: 0.1996 - categorical_accuracy: 0.9382 - val_loss: 0.2089 - val_categorical_accuracy: 0.9288 - 390ms/epoch - 20ms/step
Epoch 536/1000
20/20 - 1s - loss: 0.1479 - categorical_accuracy: 0.9500 - val_loss: 0.1937 - val_categorical_accuracy: 0.9344 - 868ms/epoch - 43ms/step
Epoch 537/1000
20/20 - 1s - loss: 0.2079 - categorical_accuracy: 0.9260 - val_loss: 0.2244 - val_categorical_accuracy: 0.9218 - 698ms/epoch - 35ms/step
Epoch 538/1000
20/20 - 1s - loss: 0.1475 - categorical_accuracy: 0.9507 - val_loss: 0.2024 - val_categorical_accuracy: 0.9300 - 637ms/epoch - 32ms/step
Epoch 539/1000
20/20 - 1s - loss: 0.1463 - categorical_accuracy: 0.9502 - val_loss: 0.1883 - val_categorical_accuracy: 0.9359 - 780ms/epoch - 39ms/step
Epoch 540/1000
20/20 - 4s - loss: 0.1489 - categorical_accuracy: 0.9484 - val_loss: 0.2951 - val_categorical_accuracy: 0.8986 - 4s/epoch - 212ms/step
Epoch 541/1000
20/20 - 1s - loss: 0.1890 - categorical_accuracy: 0.9313 - val_loss: 0.1913 - val_categorical_accuracy: 0.9351 - 694ms/epoch - 35ms/step
Epoch 542/1000
20/20 - 1s - loss: 0.1570 - categorical_accuracy: 0.9454 - val_loss: 0.2311 - val_categorical_accuracy: 0.9209 - 885ms/epoch - 44ms/step
Epoch 543/1000
20/20 - 1s - loss: 0.1577 - categorical_accuracy: 0.9444 - val_loss: 0.2037 - val_categorical_accuracy: 0.9312 - 1s/epoch - 57ms/step
Epoch 544/1000
20/20 - 1s - loss: 0.1441 - categorical_accuracy: 0.9511 - val_loss: 0.2372 - val_categorical_accuracy: 0.9205 - 1s/epoch - 68ms/step
Epoch 545/1000
20/20 - 1s - loss: 0.1534 - categorical_accuracy: 0.9467 - val_loss: 0.2460 - val_categorical_accuracy: 0.9141 - 645ms/epoch - 32ms/step
Epoch 546/1000
20/20 - 1s - loss: 0.1929 - categorical_accuracy: 0.9303 - val_loss: 0.1942 - val_categorical_accuracy: 0.9337 - 622ms/epoch - 31ms/step
Epoch 547/1000
20/20 - 1s - loss: 0.1474 - categorical_accuracy: 0.9499 - val_loss: 0.2664 - val_categorical_accuracy: 0.9127 - 1s/epoch - 55ms/step
Epoch 548/1000
20/20 - 1s - loss: 0.6481 - categorical_accuracy: 0.8408 - val_loss: 0.2196 - val_categorical_accuracy: 0.9252 - 1s/epoch - 73ms/step
Epoch 549/1000
20/20 - 2s - loss: 0.1591 - categorical_accuracy: 0.9481 - val_loss: 0.1919 - val_categorical_accuracy: 0.9349 - 2s/epoch - 80ms/step
Epoch 550/1000
20/20 - 1s - loss: 0.1418 - categorical_accuracy: 0.9534 - val_loss: 0.1883 - val_categorical_accuracy: 0.9362 - 695ms/epoch - 35ms/step
Epoch 551/1000
20/20 - 1s - loss: 0.1342 - categorical_accuracy: 0.9559 - val_loss: 0.1869 - val_categorical_accuracy: 0.9368 - 590ms/epoch - 29ms/step
Epoch 552/1000
20/20 - 1s - loss: 0.1691 - categorical_accuracy: 0.9401 - val_loss: 0.3537 - val_categorical_accuracy: 0.8777 - 785ms/epoch - 39ms/step
Epoch 553/1000
20/20 - 1s - loss: 0.1737 - categorical_accuracy: 0.9397 - val_loss: 0.1988 - val_categorical_accuracy: 0.9313 - 900ms/epoch - 45ms/step
Epoch 554/1000
20/20 - 15s - loss: 0.1438 - categorical_accuracy: 0.9509 - val_loss: 0.1877 - val_categorical_accuracy: 0.9361 - 15s/epoch - 752ms/step
Epoch 555/1000
20/20 - 1s - loss: 0.1364 - categorical_accuracy: 0.9546 - val_loss: 0.1934 - val_categorical_accuracy: 0.9340 - 675ms/epoch - 34ms/step
Epoch 556/1000
20/20 - 1s - loss: 0.1483 - categorical_accuracy: 0.9492 - val_loss: 0.1889 - val_categorical_accuracy: 0.9350 - 933ms/epoch - 47ms/step
Epoch 557/1000
20/20 - 1s - loss: 0.1446 - categorical_accuracy: 0.9503 - val_loss: 0.3586 - val_categorical_accuracy: 0.8792 - 829ms/epoch - 41ms/step
Epoch 558/1000
20/20 - 1s - loss: 0.1949 - categorical_accuracy: 0.9323 - val_loss: 0.1795 - val_categorical_accuracy: 0.9391 - 992ms/epoch - 50ms/step
Epoch 559/1000
20/20 - 0s - loss: 0.1339 - categorical_accuracy: 0.9555 - val_loss: 0.2156 - val_categorical_accuracy: 0.9240 - 333ms/epoch - 17ms/step
Epoch 560/1000
20/20 - 0s - loss: 0.1896 - categorical_accuracy: 0.9312 - val_loss: 0.1793 - val_categorical_accuracy: 0.9394 - 348ms/epoch - 17ms/step
Epoch 561/1000
20/20 - 0s - loss: 0.1348 - categorical_accuracy: 0.9548 - val_loss: 0.1830 - val_categorical_accuracy: 0.9382 - 333ms/epoch - 17ms/step
Epoch 562/1000
20/20 - 0s - loss: 0.1258 - categorical_accuracy: 0.9588 - val_loss: 0.1856 - val_categorical_accuracy: 0.9380 - 334ms/epoch - 17ms/step
Epoch 563/1000
20/20 - 0s - loss: 0.1543 - categorical_accuracy: 0.9459 - val_loss: 0.1943 - val_categorical_accuracy: 0.9347 - 331ms/epoch - 17ms/step
Epoch 564/1000
20/20 - 0s - loss: 0.1434 - categorical_accuracy: 0.9507 - val_loss: 0.2068 - val_categorical_accuracy: 0.9297 - 348ms/epoch - 17ms/step
Epoch 565/1000
20/20 - 0s - loss: 0.1736 - categorical_accuracy: 0.9370 - val_loss: 0.2883 - val_categorical_accuracy: 0.8999 - 347ms/epoch - 17ms/step
Epoch 566/1000
20/20 - 0s - loss: 0.1496 - categorical_accuracy: 0.9483 - val_loss: 0.1821 - val_categorical_accuracy: 0.9387 - 347ms/epoch - 17ms/step
Epoch 567/1000
20/20 - 0s - loss: 0.1961 - categorical_accuracy: 0.9301 - val_loss: 0.1805 - val_categorical_accuracy: 0.9387 - 358ms/epoch - 18ms/step
Epoch 568/1000
20/20 - 0s - loss: 0.1293 - categorical_accuracy: 0.9575 - val_loss: 0.2265 - val_categorical_accuracy: 0.9237 - 359ms/epoch - 18ms/step
Epoch 569/1000
20/20 - 0s - loss: 0.1435 - categorical_accuracy: 0.9506 - val_loss: 0.2047 - val_categorical_accuracy: 0.9310 - 363ms/epoch - 18ms/step
Epoch 570/1000
20/20 - 0s - loss: 0.1475 - categorical_accuracy: 0.9484 - val_loss: 0.2001 - val_categorical_accuracy: 0.9315 - 369ms/epoch - 18ms/step
Epoch 571/1000
20/20 - 0s - loss: 0.1374 - categorical_accuracy: 0.9531 - val_loss: 0.2178 - val_categorical_accuracy: 0.9261 - 341ms/epoch - 17ms/step
Epoch 572/1000
20/20 - 0s - loss: 0.2233 - categorical_accuracy: 0.9215 - val_loss: 0.1923 - val_categorical_accuracy: 0.9341 - 356ms/epoch - 18ms/step
Epoch 573/1000
20/20 - 0s - loss: 0.1274 - categorical_accuracy: 0.9584 - val_loss: 0.1952 - val_categorical_accuracy: 0.9343 - 358ms/epoch - 18ms/step
Epoch 574/1000
20/20 - 0s - loss: 0.1236 - categorical_accuracy: 0.9597 - val_loss: 0.1746 - val_categorical_accuracy: 0.9417 - 336ms/epoch - 17ms/step
Epoch 575/1000
20/20 - 0s - loss: 0.1400 - categorical_accuracy: 0.9518 - val_loss: 0.2091 - val_categorical_accuracy: 0.9263 - 353ms/epoch - 18ms/step
Epoch 576/1000
20/20 - 0s - loss: 0.2179 - categorical_accuracy: 0.9236 - val_loss: 0.1763 - val_categorical_accuracy: 0.9408 - 326ms/epoch - 16ms/step
Epoch 577/1000
20/20 - 0s - loss: 0.1361 - categorical_accuracy: 0.9540 - val_loss: 0.1949 - val_categorical_accuracy: 0.9338 - 330ms/epoch - 17ms/step
Epoch 578/1000
20/20 - 0s - loss: 0.1402 - categorical_accuracy: 0.9521 - val_loss: 0.1799 - val_categorical_accuracy: 0.9394 - 351ms/epoch - 18ms/step
Epoch 579/1000
20/20 - 0s - loss: 0.1340 - categorical_accuracy: 0.9542 - val_loss: 0.1789 - val_categorical_accuracy: 0.9396 - 364ms/epoch - 18ms/step
Epoch 580/1000
20/20 - 0s - loss: 0.1378 - categorical_accuracy: 0.9532 - val_loss: 0.1956 - val_categorical_accuracy: 0.9334 - 348ms/epoch - 17ms/step
Epoch 581/1000
20/20 - 0s - loss: 0.1426 - categorical_accuracy: 0.9509 - val_loss: 0.1928 - val_categorical_accuracy: 0.9335 - 362ms/epoch - 18ms/step
Epoch 582/1000
20/20 - 0s - loss: 0.1402 - categorical_accuracy: 0.9517 - val_loss: 0.1961 - val_categorical_accuracy: 0.9325 - 343ms/epoch - 17ms/step
Epoch 583/1000
20/20 - 0s - loss: 0.1433 - categorical_accuracy: 0.9500 - val_loss: 0.1822 - val_categorical_accuracy: 0.9393 - 353ms/epoch - 18ms/step
Epoch 584/1000
20/20 - 0s - loss: 0.1358 - categorical_accuracy: 0.9536 - val_loss: 0.2621 - val_categorical_accuracy: 0.9080 - 366ms/epoch - 18ms/step
Epoch 585/1000
20/20 - 0s - loss: 0.2075 - categorical_accuracy: 0.9295 - val_loss: 0.1738 - val_categorical_accuracy: 0.9415 - 350ms/epoch - 18ms/step
Epoch 586/1000
20/20 - 0s - loss: 0.1728 - categorical_accuracy: 0.9414 - val_loss: 0.6178 - val_categorical_accuracy: 0.8377 - 349ms/epoch - 17ms/step
Epoch 587/1000
20/20 - 0s - loss: 0.4793 - categorical_accuracy: 0.8752 - val_loss: 0.1922 - val_categorical_accuracy: 0.9348 - 358ms/epoch - 18ms/step
Epoch 588/1000
20/20 - 0s - loss: 0.1342 - categorical_accuracy: 0.9561 - val_loss: 0.1802 - val_categorical_accuracy: 0.9394 - 352ms/epoch - 18ms/step
Epoch 589/1000
20/20 - 0s - loss: 0.1275 - categorical_accuracy: 0.9580 - val_loss: 0.1774 - val_categorical_accuracy: 0.9403 - 349ms/epoch - 17ms/step
Epoch 590/1000
20/20 - 0s - loss: 0.1317 - categorical_accuracy: 0.9559 - val_loss: 0.1869 - val_categorical_accuracy: 0.9365 - 364ms/epoch - 18ms/step
Epoch 591/1000
20/20 - 0s - loss: 0.1954 - categorical_accuracy: 0.9289 - val_loss: 0.4019 - val_categorical_accuracy: 0.8677 - 349ms/epoch - 17ms/step
Epoch 592/1000
20/20 - 0s - loss: 0.1456 - categorical_accuracy: 0.9510 - val_loss: 0.1736 - val_categorical_accuracy: 0.9420 - 348ms/epoch - 17ms/step
Epoch 593/1000
20/20 - 0s - loss: 0.1320 - categorical_accuracy: 0.9555 - val_loss: 0.2288 - val_categorical_accuracy: 0.9195 - 338ms/epoch - 17ms/step
Epoch 594/1000
20/20 - 0s - loss: 0.1570 - categorical_accuracy: 0.9447 - val_loss: 0.1816 - val_categorical_accuracy: 0.9391 - 349ms/epoch - 17ms/step
Epoch 595/1000
20/20 - 0s - loss: 0.1432 - categorical_accuracy: 0.9496 - val_loss: 0.1904 - val_categorical_accuracy: 0.9356 - 358ms/epoch - 18ms/step
Epoch 596/1000
20/20 - 0s - loss: 0.1242 - categorical_accuracy: 0.9587 - val_loss: 0.1795 - val_categorical_accuracy: 0.9395 - 373ms/epoch - 19ms/step
Epoch 597/1000
20/20 - 0s - loss: 0.1473 - categorical_accuracy: 0.9487 - val_loss: 0.3548 - val_categorical_accuracy: 0.8818 - 347ms/epoch - 17ms/step
Epoch 598/1000
20/20 - 0s - loss: 0.1745 - categorical_accuracy: 0.9398 - val_loss: 0.1719 - val_categorical_accuracy: 0.9424 - 389ms/epoch - 19ms/step
Epoch 599/1000
20/20 - 0s - loss: 0.1238 - categorical_accuracy: 0.9589 - val_loss: 0.2040 - val_categorical_accuracy: 0.9281 - 389ms/epoch - 19ms/step
Epoch 600/1000
20/20 - 0s - loss: 0.1749 - categorical_accuracy: 0.9356 - val_loss: 0.1896 - val_categorical_accuracy: 0.9362 - 368ms/epoch - 18ms/step
Epoch 601/1000
20/20 - 0s - loss: 0.1190 - categorical_accuracy: 0.9611 - val_loss: 0.1859 - val_categorical_accuracy: 0.9380 - 470ms/epoch - 24ms/step
Epoch 602/1000
20/20 - 0s - loss: 0.1281 - categorical_accuracy: 0.9573 - val_loss: 0.1789 - val_categorical_accuracy: 0.9404 - 460ms/epoch - 23ms/step
Epoch 603/1000
20/20 - 0s - loss: 0.1306 - categorical_accuracy: 0.9558 - val_loss: 0.2751 - val_categorical_accuracy: 0.9082 - 365ms/epoch - 18ms/step
Epoch 604/1000
20/20 - 0s - loss: 0.2017 - categorical_accuracy: 0.9319 - val_loss: 0.1735 - val_categorical_accuracy: 0.9417 - 337ms/epoch - 17ms/step
Epoch 605/1000
20/20 - 0s - loss: 0.1166 - categorical_accuracy: 0.9620 - val_loss: 0.1743 - val_categorical_accuracy: 0.9420 - 340ms/epoch - 17ms/step
Epoch 606/1000
20/20 - 0s - loss: 0.2139 - categorical_accuracy: 0.9261 - val_loss: 0.2836 - val_categorical_accuracy: 0.8982 - 346ms/epoch - 17ms/step
Epoch 607/1000
20/20 - 0s - loss: 0.1362 - categorical_accuracy: 0.9542 - val_loss: 0.1759 - val_categorical_accuracy: 0.9413 - 321ms/epoch - 16ms/step
Epoch 608/1000
20/20 - 0s - loss: 0.1260 - categorical_accuracy: 0.9577 - val_loss: 0.1716 - val_categorical_accuracy: 0.9429 - 350ms/epoch - 18ms/step
Epoch 609/1000
20/20 - 0s - loss: 0.1174 - categorical_accuracy: 0.9618 - val_loss: 0.1746 - val_categorical_accuracy: 0.9423 - 362ms/epoch - 18ms/step
Epoch 610/1000
20/20 - 0s - loss: 0.1392 - categorical_accuracy: 0.9524 - val_loss: 0.1812 - val_categorical_accuracy: 0.9386 - 364ms/epoch - 18ms/step
Epoch 611/1000
20/20 - 0s - loss: 0.1301 - categorical_accuracy: 0.9559 - val_loss: 0.1836 - val_categorical_accuracy: 0.9375 - 365ms/epoch - 18ms/step
Epoch 612/1000
20/20 - 0s - loss: 0.2003 - categorical_accuracy: 0.9310 - val_loss: 0.1731 - val_categorical_accuracy: 0.9418 - 336ms/epoch - 17ms/step
Epoch 613/1000
20/20 - 1s - loss: 0.1179 - categorical_accuracy: 0.9614 - val_loss: 0.1817 - val_categorical_accuracy: 0.9383 - 735ms/epoch - 37ms/step
Epoch 614/1000
20/20 - 5s - loss: 0.1157 - categorical_accuracy: 0.9616 - val_loss: 0.1758 - val_categorical_accuracy: 0.9412 - 5s/epoch - 264ms/step
Epoch 615/1000
20/20 - 1s - loss: 0.2251 - categorical_accuracy: 0.9246 - val_loss: 0.3756 - val_categorical_accuracy: 0.8765 - 1s/epoch - 69ms/step
Epoch 616/1000
20/20 - 3s - loss: 0.1395 - categorical_accuracy: 0.9538 - val_loss: 0.1701 - val_categorical_accuracy: 0.9433 - 3s/epoch - 159ms/step
Epoch 617/1000
20/20 - 1s - loss: 0.1231 - categorical_accuracy: 0.9590 - val_loss: 0.2174 - val_categorical_accuracy: 0.9279 - 740ms/epoch - 37ms/step
Epoch 618/1000
20/20 - 4s - loss: 0.1282 - categorical_accuracy: 0.9566 - val_loss: 0.1703 - val_categorical_accuracy: 0.9432 - 4s/epoch - 178ms/step
Epoch 619/1000
20/20 - 4s - loss: 0.1151 - categorical_accuracy: 0.9617 - val_loss: 0.1861 - val_categorical_accuracy: 0.9380 - 4s/epoch - 177ms/step
Epoch 620/1000
20/20 - 4s - loss: 0.1965 - categorical_accuracy: 0.9308 - val_loss: 0.1728 - val_categorical_accuracy: 0.9415 - 4s/epoch - 176ms/step
Epoch 621/1000
20/20 - 4s - loss: 0.1132 - categorical_accuracy: 0.9634 - val_loss: 0.1692 - val_categorical_accuracy: 0.9434 - 4s/epoch - 176ms/step
Epoch 622/1000
20/20 - 3s - loss: 0.3036 - categorical_accuracy: 0.9181 - val_loss: 0.2141 - val_categorical_accuracy: 0.9270 - 3s/epoch - 172ms/step
Epoch 623/1000
20/20 - 3s - loss: 0.1313 - categorical_accuracy: 0.9567 - val_loss: 0.1729 - val_categorical_accuracy: 0.9421 - 3s/epoch - 175ms/step
Epoch 624/1000
20/20 - 3s - loss: 0.1140 - categorical_accuracy: 0.9629 - val_loss: 0.1745 - val_categorical_accuracy: 0.9416 - 3s/epoch - 174ms/step
Epoch 625/1000
20/20 - 4s - loss: 0.1383 - categorical_accuracy: 0.9523 - val_loss: 0.3194 - val_categorical_accuracy: 0.8851 - 4s/epoch - 181ms/step
Epoch 626/1000
20/20 - 3s - loss: 0.1732 - categorical_accuracy: 0.9377 - val_loss: 0.1710 - val_categorical_accuracy: 0.9431 - 3s/epoch - 173ms/step
Epoch 627/1000
20/20 - 4s - loss: 0.1208 - categorical_accuracy: 0.9598 - val_loss: 0.2005 - val_categorical_accuracy: 0.9336 - 4s/epoch - 179ms/step
Epoch 628/1000
20/20 - 4s - loss: 0.1303 - categorical_accuracy: 0.9555 - val_loss: 0.1901 - val_categorical_accuracy: 0.9371 - 4s/epoch - 183ms/step
Epoch 629/1000
20/20 - 3s - loss: 0.1186 - categorical_accuracy: 0.9601 - val_loss: 0.1905 - val_categorical_accuracy: 0.9368 - 3s/epoch - 138ms/step
Epoch 630/1000
20/20 - 3s - loss: 0.1409 - categorical_accuracy: 0.9495 - val_loss: 0.2776 - val_categorical_accuracy: 0.9061 - 3s/epoch - 138ms/step
Epoch 631/1000
20/20 - 3s - loss: 1.9316 - categorical_accuracy: 0.6149 - val_loss: 1.1333 - val_categorical_accuracy: 0.6174 - 3s/epoch - 158ms/step
Epoch 632/1000
20/20 - 3s - loss: 0.7522 - categorical_accuracy: 0.7436 - val_loss: 0.5203 - val_categorical_accuracy: 0.8169 - 3s/epoch - 135ms/step
Epoch 633/1000
20/20 - 3s - loss: 0.4081 - categorical_accuracy: 0.8555 - val_loss: 0.3511 - val_categorical_accuracy: 0.8775 - 3s/epoch - 153ms/step
Epoch 634/1000
20/20 - 3s - loss: 0.2814 - categorical_accuracy: 0.9022 - val_loss: 0.2756 - val_categorical_accuracy: 0.9045 - 3s/epoch - 150ms/step
Epoch 635/1000
20/20 - 3s - loss: 0.2196 - categorical_accuracy: 0.9249 - val_loss: 0.2410 - val_categorical_accuracy: 0.9177 - 3s/epoch - 147ms/step
Epoch 636/1000
20/20 - 3s - loss: 0.1883 - categorical_accuracy: 0.9368 - val_loss: 0.2346 - val_categorical_accuracy: 0.9191 - 3s/epoch - 156ms/step
Epoch 637/1000
20/20 - 3s - loss: 0.1825 - categorical_accuracy: 0.9374 - val_loss: 0.2092 - val_categorical_accuracy: 0.9293 - 3s/epoch - 151ms/step
Epoch 638/1000
20/20 - 3s - loss: 0.1559 - categorical_accuracy: 0.9485 - val_loss: 0.2047 - val_categorical_accuracy: 0.9302 - 3s/epoch - 128ms/step
Epoch 639/1000
20/20 - 3s - loss: 0.1495 - categorical_accuracy: 0.9497 - val_loss: 0.2099 - val_categorical_accuracy: 0.9289 - 3s/epoch - 156ms/step
Epoch 640/1000
20/20 - 3s - loss: 0.1742 - categorical_accuracy: 0.9389 - val_loss: 0.1969 - val_categorical_accuracy: 0.9334 - 3s/epoch - 155ms/step
Epoch 641/1000
20/20 - 3s - loss: 0.1408 - categorical_accuracy: 0.9529 - val_loss: 0.1908 - val_categorical_accuracy: 0.9361 - 3s/epoch - 154ms/step
Epoch 642/1000
20/20 - 3s - loss: 0.1354 - categorical_accuracy: 0.9554 - val_loss: 0.1842 - val_categorical_accuracy: 0.9383 - 3s/epoch - 151ms/step
Epoch 643/1000
20/20 - 3s - loss: 0.1531 - categorical_accuracy: 0.9468 - val_loss: 0.3349 - val_categorical_accuracy: 0.8869 - 3s/epoch - 158ms/step
Epoch 644/1000
20/20 - 3s - loss: 0.1576 - categorical_accuracy: 0.9462 - val_loss: 0.1871 - val_categorical_accuracy: 0.9375 - 3s/epoch - 155ms/step
Epoch 645/1000
20/20 - 3s - loss: 0.1268 - categorical_accuracy: 0.9586 - val_loss: 0.1882 - val_categorical_accuracy: 0.9376 - 3s/epoch - 164ms/step
Epoch 646/1000
20/20 - 2s - loss: 0.1581 - categorical_accuracy: 0.9438 - val_loss: 0.2417 - val_categorical_accuracy: 0.9177 - 2s/epoch - 121ms/step
Epoch 647/1000
20/20 - 3s - loss: 0.1296 - categorical_accuracy: 0.9566 - val_loss: 0.1789 - val_categorical_accuracy: 0.9402 - 3s/epoch - 156ms/step
Epoch 648/1000
20/20 - 3s - loss: 0.1243 - categorical_accuracy: 0.9586 - val_loss: 0.2045 - val_categorical_accuracy: 0.9294 - 3s/epoch - 161ms/step
Epoch 649/1000
20/20 - 3s - loss: 0.1724 - categorical_accuracy: 0.9388 - val_loss: 0.1747 - val_categorical_accuracy: 0.9417 - 3s/epoch - 157ms/step
Epoch 650/1000
20/20 - 3s - loss: 0.1220 - categorical_accuracy: 0.9599 - val_loss: 0.2046 - val_categorical_accuracy: 0.9310 - 3s/epoch - 159ms/step
Epoch 651/1000
20/20 - 3s - loss: 0.1727 - categorical_accuracy: 0.9372 - val_loss: 0.1739 - val_categorical_accuracy: 0.9419 - 3s/epoch - 161ms/step
Epoch 652/1000
20/20 - 3s - loss: 0.1164 - categorical_accuracy: 0.9624 - val_loss: 0.1836 - val_categorical_accuracy: 0.9388 - 3s/epoch - 159ms/step
Epoch 653/1000
20/20 - 3s - loss: 0.1291 - categorical_accuracy: 0.9561 - val_loss: 0.1863 - val_categorical_accuracy: 0.9389 - 3s/epoch - 159ms/step
Epoch 654/1000
20/20 - 7s - loss: 0.1198 - categorical_accuracy: 0.9604 - val_loss: 0.1888 - val_categorical_accuracy: 0.9376 - 7s/epoch - 334ms/step
Epoch 655/1000
20/20 - 3s - loss: 0.1838 - categorical_accuracy: 0.9351 - val_loss: 0.2299 - val_categorical_accuracy: 0.9216 - 3s/epoch - 158ms/step
Epoch 656/1000
20/20 - 3s - loss: 0.1291 - categorical_accuracy: 0.9567 - val_loss: 0.2138 - val_categorical_accuracy: 0.9248 - 3s/epoch - 169ms/step
Epoch 657/1000
20/20 - 3s - loss: 0.1291 - categorical_accuracy: 0.9563 - val_loss: 0.1748 - val_categorical_accuracy: 0.9420 - 3s/epoch - 169ms/step
Epoch 658/1000
20/20 - 3s - loss: 0.1161 - categorical_accuracy: 0.9621 - val_loss: 0.1824 - val_categorical_accuracy: 0.9395 - 3s/epoch - 166ms/step
Epoch 659/1000
20/20 - 3s - loss: 0.2066 - categorical_accuracy: 0.9256 - val_loss: 0.1900 - val_categorical_accuracy: 0.9362 - 3s/epoch - 169ms/step
Epoch 660/1000
20/20 - 3s - loss: 0.1165 - categorical_accuracy: 0.9622 - val_loss: 0.1736 - val_categorical_accuracy: 0.9416 - 3s/epoch - 150ms/step
Epoch 661/1000
20/20 - 4s - loss: 0.1280 - categorical_accuracy: 0.9566 - val_loss: 0.3365 - val_categorical_accuracy: 0.8871 - 4s/epoch - 178ms/step
Epoch 662/1000
20/20 - 6s - loss: 0.1637 - categorical_accuracy: 0.9444 - val_loss: 0.1691 - val_categorical_accuracy: 0.9442 - 6s/epoch - 288ms/step
Epoch 663/1000
20/20 - 17s - loss: 0.1130 - categorical_accuracy: 0.9632 - val_loss: 0.1692 - val_categorical_accuracy: 0.9438 - 17s/epoch - 826ms/step
Epoch 664/1000
20/20 - 0s - loss: 0.1135 - categorical_accuracy: 0.9626 - val_loss: 0.1851 - val_categorical_accuracy: 0.9369 - 359ms/epoch - 18ms/step
Epoch 665/1000
20/20 - 0s - loss: 0.1570 - categorical_accuracy: 0.9430 - val_loss: 0.1698 - val_categorical_accuracy: 0.9443 - 330ms/epoch - 17ms/step
Epoch 666/1000
20/20 - 0s - loss: 0.1113 - categorical_accuracy: 0.9636 - val_loss: 0.1896 - val_categorical_accuracy: 0.9375 - 340ms/epoch - 17ms/step
Epoch 667/1000
20/20 - 0s - loss: 0.1545 - categorical_accuracy: 0.9444 - val_loss: 0.1910 - val_categorical_accuracy: 0.9373 - 346ms/epoch - 17ms/step
Epoch 668/1000
20/20 - 0s - loss: 0.1118 - categorical_accuracy: 0.9630 - val_loss: 0.1835 - val_categorical_accuracy: 0.9397 - 356ms/epoch - 18ms/step
Epoch 669/1000
20/20 - 0s - loss: 0.1267 - categorical_accuracy: 0.9568 - val_loss: 0.1811 - val_categorical_accuracy: 0.9411 - 338ms/epoch - 17ms/step
Epoch 670/1000
20/20 - 3s - loss: 0.1117 - categorical_accuracy: 0.9631 - val_loss: 0.1766 - val_categorical_accuracy: 0.9417 - 3s/epoch - 129ms/step
Epoch 671/1000
20/20 - 3s - loss: 0.1520 - categorical_accuracy: 0.9463 - val_loss: 0.3362 - val_categorical_accuracy: 0.8893 - 3s/epoch - 160ms/step
Epoch 672/1000
20/20 - 27s - loss: 0.1413 - categorical_accuracy: 0.9513 - val_loss: 0.1691 - val_categorical_accuracy: 0.9443 - 27s/epoch - 1s/step
Epoch 673/1000
20/20 - 0s - loss: 0.1088 - categorical_accuracy: 0.9646 - val_loss: 0.1796 - val_categorical_accuracy: 0.9404 - 473ms/epoch - 24ms/step
Epoch 674/1000
20/20 - 1s - loss: 0.1159 - categorical_accuracy: 0.9618 - val_loss: 0.1682 - val_categorical_accuracy: 0.9442 - 690ms/epoch - 35ms/step
Epoch 675/1000
20/20 - 1s - loss: 0.1132 - categorical_accuracy: 0.9625 - val_loss: 0.1797 - val_categorical_accuracy: 0.9402 - 631ms/epoch - 32ms/step
Epoch 676/1000
20/20 - 0s - loss: 0.2360 - categorical_accuracy: 0.9260 - val_loss: 0.1842 - val_categorical_accuracy: 0.9389 - 359ms/epoch - 18ms/step
Epoch 677/1000
20/20 - 0s - loss: 0.1115 - categorical_accuracy: 0.9640 - val_loss: 0.1669 - val_categorical_accuracy: 0.9443 - 313ms/epoch - 16ms/step
Epoch 678/1000
20/20 - 0s - loss: 0.1112 - categorical_accuracy: 0.9633 - val_loss: 0.2227 - val_categorical_accuracy: 0.9281 - 333ms/epoch - 17ms/step
Epoch 679/1000
20/20 - 0s - loss: 0.1154 - categorical_accuracy: 0.9615 - val_loss: 0.1660 - val_categorical_accuracy: 0.9465 - 335ms/epoch - 17ms/step
Epoch 680/1000
20/20 - 0s - loss: 0.1057 - categorical_accuracy: 0.9655 - val_loss: 0.1756 - val_categorical_accuracy: 0.9410 - 351ms/epoch - 18ms/step
Epoch 681/1000
20/20 - 0s - loss: 0.1395 - categorical_accuracy: 0.9508 - val_loss: 0.1873 - val_categorical_accuracy: 0.9373 - 330ms/epoch - 17ms/step
Epoch 682/1000
20/20 - 0s - loss: 0.1187 - categorical_accuracy: 0.9603 - val_loss: 0.1910 - val_categorical_accuracy: 0.9382 - 333ms/epoch - 17ms/step
Epoch 683/1000
20/20 - 0s - loss: 0.1063 - categorical_accuracy: 0.9652 - val_loss: 0.1705 - val_categorical_accuracy: 0.9432 - 349ms/epoch - 17ms/step
Epoch 684/1000
20/20 - 0s - loss: 0.2062 - categorical_accuracy: 0.9363 - val_loss: 0.1715 - val_categorical_accuracy: 0.9431 - 343ms/epoch - 17ms/step
Epoch 685/1000
20/20 - 0s - loss: 0.1035 - categorical_accuracy: 0.9665 - val_loss: 0.1645 - val_categorical_accuracy: 0.9459 - 363ms/epoch - 18ms/step
Epoch 686/1000
20/20 - 0s - loss: 0.1192 - categorical_accuracy: 0.9598 - val_loss: 0.2256 - val_categorical_accuracy: 0.9271 - 373ms/epoch - 19ms/step
Epoch 687/1000
20/20 - 0s - loss: 0.1283 - categorical_accuracy: 0.9550 - val_loss: 0.3013 - val_categorical_accuracy: 0.9003 - 379ms/epoch - 19ms/step
Epoch 688/1000
20/20 - 0s - loss: 0.2009 - categorical_accuracy: 0.9339 - val_loss: 0.1670 - val_categorical_accuracy: 0.9454 - 378ms/epoch - 19ms/step
Epoch 689/1000
20/20 - 0s - loss: 0.1040 - categorical_accuracy: 0.9666 - val_loss: 0.1732 - val_categorical_accuracy: 0.9427 - 368ms/epoch - 18ms/step
Epoch 690/1000
20/20 - 0s - loss: 0.1141 - categorical_accuracy: 0.9620 - val_loss: 0.1895 - val_categorical_accuracy: 0.9361 - 366ms/epoch - 18ms/step
Epoch 691/1000
20/20 - 0s - loss: 0.1818 - categorical_accuracy: 0.9349 - val_loss: 0.1639 - val_categorical_accuracy: 0.9460 - 365ms/epoch - 18ms/step
Epoch 692/1000
20/20 - 0s - loss: 0.1075 - categorical_accuracy: 0.9647 - val_loss: 0.1665 - val_categorical_accuracy: 0.9445 - 358ms/epoch - 18ms/step
Epoch 693/1000
20/20 - 0s - loss: 0.1099 - categorical_accuracy: 0.9638 - val_loss: 0.1991 - val_categorical_accuracy: 0.9347 - 363ms/epoch - 18ms/step
Epoch 694/1000
20/20 - 0s - loss: 0.1330 - categorical_accuracy: 0.9536 - val_loss: 0.1739 - val_categorical_accuracy: 0.9430 - 373ms/epoch - 19ms/step
Epoch 695/1000
20/20 - 0s - loss: 0.1021 - categorical_accuracy: 0.9669 - val_loss: 0.1698 - val_categorical_accuracy: 0.9440 - 375ms/epoch - 19ms/step
Epoch 696/1000
20/20 - 0s - loss: 0.1175 - categorical_accuracy: 0.9596 - val_loss: 0.1656 - val_categorical_accuracy: 0.9453 - 349ms/epoch - 17ms/step
Epoch 697/1000
20/20 - 0s - loss: 0.1196 - categorical_accuracy: 0.9595 - val_loss: 0.2215 - val_categorical_accuracy: 0.9228 - 363ms/epoch - 18ms/step
Epoch 698/1000
20/20 - 0s - loss: 0.2084 - categorical_accuracy: 0.9299 - val_loss: 0.1630 - val_categorical_accuracy: 0.9468 - 354ms/epoch - 18ms/step
Epoch 699/1000
20/20 - 0s - loss: 0.1008 - categorical_accuracy: 0.9676 - val_loss: 0.1646 - val_categorical_accuracy: 0.9466 - 364ms/epoch - 18ms/step
Epoch 700/1000
20/20 - 0s - loss: 0.1121 - categorical_accuracy: 0.9622 - val_loss: 0.1957 - val_categorical_accuracy: 0.9355 - 382ms/epoch - 19ms/step
Epoch 701/1000
20/20 - 0s - loss: 0.1229 - categorical_accuracy: 0.9573 - val_loss: 0.1717 - val_categorical_accuracy: 0.9440 - 363ms/epoch - 18ms/step
Epoch 702/1000
20/20 - 0s - loss: 0.1090 - categorical_accuracy: 0.9639 - val_loss: 0.1728 - val_categorical_accuracy: 0.9434 - 374ms/epoch - 19ms/step
Epoch 703/1000
20/20 - 0s - loss: 0.1022 - categorical_accuracy: 0.9663 - val_loss: 0.1744 - val_categorical_accuracy: 0.9440 - 358ms/epoch - 18ms/step
Epoch 704/1000
20/20 - 0s - loss: 0.1067 - categorical_accuracy: 0.9643 - val_loss: 0.2511 - val_categorical_accuracy: 0.9199 - 421ms/epoch - 21ms/step
Epoch 705/1000
20/20 - 0s - loss: 0.1954 - categorical_accuracy: 0.9365 - val_loss: 0.1660 - val_categorical_accuracy: 0.9457 - 405ms/epoch - 20ms/step
Epoch 706/1000
20/20 - 0s - loss: 0.0992 - categorical_accuracy: 0.9676 - val_loss: 0.1680 - val_categorical_accuracy: 0.9448 - 392ms/epoch - 20ms/step
Epoch 707/1000
20/20 - 0s - loss: 0.8672 - categorical_accuracy: 0.7985 - val_loss: 0.3441 - val_categorical_accuracy: 0.8829 - 387ms/epoch - 19ms/step
Epoch 708/1000
20/20 - 0s - loss: 0.1981 - categorical_accuracy: 0.9336 - val_loss: 0.1921 - val_categorical_accuracy: 0.9345 - 383ms/epoch - 19ms/step
Epoch 709/1000
20/20 - 0s - loss: 0.1284 - categorical_accuracy: 0.9581 - val_loss: 0.1760 - val_categorical_accuracy: 0.9416 - 383ms/epoch - 19ms/step
Epoch 710/1000
20/20 - 0s - loss: 0.1131 - categorical_accuracy: 0.9634 - val_loss: 0.1689 - val_categorical_accuracy: 0.9450 - 397ms/epoch - 20ms/step
Epoch 711/1000
20/20 - 0s - loss: 0.1066 - categorical_accuracy: 0.9660 - val_loss: 0.1663 - val_categorical_accuracy: 0.9453 - 389ms/epoch - 19ms/step
Epoch 712/1000
20/20 - 0s - loss: 0.1123 - categorical_accuracy: 0.9626 - val_loss: 0.1891 - val_categorical_accuracy: 0.9380 - 397ms/epoch - 20ms/step
Epoch 713/1000
20/20 - 0s - loss: 0.1153 - categorical_accuracy: 0.9609 - val_loss: 0.1708 - val_categorical_accuracy: 0.9448 - 396ms/epoch - 20ms/step
Epoch 714/1000
20/20 - 0s - loss: 0.1096 - categorical_accuracy: 0.9637 - val_loss: 0.1950 - val_categorical_accuracy: 0.9373 - 385ms/epoch - 19ms/step
Epoch 715/1000
20/20 - 0s - loss: 0.1228 - categorical_accuracy: 0.9576 - val_loss: 0.1859 - val_categorical_accuracy: 0.9396 - 399ms/epoch - 20ms/step
Epoch 716/1000
20/20 - 0s - loss: 0.1061 - categorical_accuracy: 0.9649 - val_loss: 0.1863 - val_categorical_accuracy: 0.9399 - 384ms/epoch - 19ms/step
Epoch 717/1000
20/20 - 0s - loss: 0.2097 - categorical_accuracy: 0.9285 - val_loss: 0.1741 - val_categorical_accuracy: 0.9413 - 390ms/epoch - 20ms/step
Epoch 718/1000
20/20 - 0s - loss: 0.1035 - categorical_accuracy: 0.9666 - val_loss: 0.1635 - val_categorical_accuracy: 0.9472 - 399ms/epoch - 20ms/step
Epoch 719/1000
20/20 - 0s - loss: 0.1054 - categorical_accuracy: 0.9651 - val_loss: 0.1861 - val_categorical_accuracy: 0.9373 - 382ms/epoch - 19ms/step
Epoch 720/1000
20/20 - 0s - loss: 0.1771 - categorical_accuracy: 0.9375 - val_loss: 0.1630 - val_categorical_accuracy: 0.9469 - 396ms/epoch - 20ms/step
Epoch 721/1000
20/20 - 0s - loss: 0.1002 - categorical_accuracy: 0.9679 - val_loss: 0.1652 - val_categorical_accuracy: 0.9462 - 395ms/epoch - 20ms/step
Epoch 722/1000
20/20 - 0s - loss: 0.1128 - categorical_accuracy: 0.9621 - val_loss: 0.2394 - val_categorical_accuracy: 0.9210 - 378ms/epoch - 19ms/step
Epoch 723/1000
20/20 - 0s - loss: 0.1325 - categorical_accuracy: 0.9533 - val_loss: 0.1961 - val_categorical_accuracy: 0.9362 - 381ms/epoch - 19ms/step
Epoch 724/1000
20/20 - 0s - loss: 0.1001 - categorical_accuracy: 0.9672 - val_loss: 0.1657 - val_categorical_accuracy: 0.9463 - 388ms/epoch - 19ms/step
Epoch 725/1000
20/20 - 0s - loss: 0.0989 - categorical_accuracy: 0.9677 - val_loss: 0.1640 - val_categorical_accuracy: 0.9464 - 385ms/epoch - 19ms/step
Epoch 726/1000
20/20 - 0s - loss: 0.1070 - categorical_accuracy: 0.9645 - val_loss: 0.1922 - val_categorical_accuracy: 0.9389 - 388ms/epoch - 19ms/step
Epoch 727/1000
20/20 - 0s - loss: 0.2074 - categorical_accuracy: 0.9290 - val_loss: 0.1642 - val_categorical_accuracy: 0.9460 - 380ms/epoch - 19ms/step
Epoch 728/1000
20/20 - 0s - loss: 0.1000 - categorical_accuracy: 0.9677 - val_loss: 0.1621 - val_categorical_accuracy: 0.9468 - 397ms/epoch - 20ms/step
Epoch 729/1000
20/20 - 0s - loss: 0.0967 - categorical_accuracy: 0.9687 - val_loss: 0.1631 - val_categorical_accuracy: 0.9468 - 387ms/epoch - 19ms/step
Epoch 730/1000
20/20 - 0s - loss: 0.1854 - categorical_accuracy: 0.9343 - val_loss: 0.1950 - val_categorical_accuracy: 0.9339 - 379ms/epoch - 19ms/step
Epoch 731/1000
20/20 - 0s - loss: 0.1021 - categorical_accuracy: 0.9670 - val_loss: 0.1655 - val_categorical_accuracy: 0.9456 - 397ms/epoch - 20ms/step
Epoch 732/1000
20/20 - 0s - loss: 0.1041 - categorical_accuracy: 0.9654 - val_loss: 0.1608 - val_categorical_accuracy: 0.9480 - 390ms/epoch - 20ms/step
Epoch 733/1000
20/20 - 0s - loss: 0.1044 - categorical_accuracy: 0.9656 - val_loss: 0.1756 - val_categorical_accuracy: 0.9435 - 396ms/epoch - 20ms/step
Epoch 734/1000
20/20 - 0s - loss: 0.0999 - categorical_accuracy: 0.9674 - val_loss: 0.1816 - val_categorical_accuracy: 0.9418 - 388ms/epoch - 19ms/step
Epoch 735/1000
20/20 - 0s - loss: 0.1271 - categorical_accuracy: 0.9551 - val_loss: 0.1956 - val_categorical_accuracy: 0.9359 - 378ms/epoch - 19ms/step
Epoch 736/1000
20/20 - 0s - loss: 0.1183 - categorical_accuracy: 0.9594 - val_loss: 0.1754 - val_categorical_accuracy: 0.9434 - 394ms/epoch - 20ms/step
Epoch 737/1000
20/20 - 0s - loss: 0.0959 - categorical_accuracy: 0.9687 - val_loss: 0.1698 - val_categorical_accuracy: 0.9452 - 384ms/epoch - 19ms/step
Epoch 738/1000
20/20 - 0s - loss: 0.1198 - categorical_accuracy: 0.9583 - val_loss: 0.2067 - val_categorical_accuracy: 0.9335 - 393ms/epoch - 20ms/step
Epoch 739/1000
20/20 - 0s - loss: 0.1006 - categorical_accuracy: 0.9666 - val_loss: 0.1644 - val_categorical_accuracy: 0.9469 - 387ms/epoch - 19ms/step
Epoch 740/1000
20/20 - 0s - loss: 0.1024 - categorical_accuracy: 0.9660 - val_loss: 0.1680 - val_categorical_accuracy: 0.9458 - 384ms/epoch - 19ms/step
Epoch 741/1000
20/20 - 0s - loss: 0.1101 - categorical_accuracy: 0.9628 - val_loss: 0.2086 - val_categorical_accuracy: 0.9269 - 387ms/epoch - 19ms/step
Epoch 742/1000
20/20 - 0s - loss: 0.2241 - categorical_accuracy: 0.9309 - val_loss: 0.1632 - val_categorical_accuracy: 0.9462 - 399ms/epoch - 20ms/step
Epoch 743/1000
20/20 - 0s - loss: 0.0973 - categorical_accuracy: 0.9685 - val_loss: 0.1650 - val_categorical_accuracy: 0.9461 - 398ms/epoch - 20ms/step
Epoch 744/1000
20/20 - 0s - loss: 0.1038 - categorical_accuracy: 0.9649 - val_loss: 0.1617 - val_categorical_accuracy: 0.9472 - 364ms/epoch - 18ms/step
Epoch 745/1000
20/20 - 0s - loss: 0.0962 - categorical_accuracy: 0.9683 - val_loss: 0.1830 - val_categorical_accuracy: 0.9410 - 381ms/epoch - 19ms/step
Epoch 746/1000
20/20 - 0s - loss: 0.1034 - categorical_accuracy: 0.9660 - val_loss: 0.1822 - val_categorical_accuracy: 0.9397 - 389ms/epoch - 19ms/step
Epoch 747/1000
20/20 - 0s - loss: 0.2005 - categorical_accuracy: 0.9358 - val_loss: 0.1651 - val_categorical_accuracy: 0.9462 - 390ms/epoch - 20ms/step
Epoch 748/1000
20/20 - 0s - loss: 0.0954 - categorical_accuracy: 0.9694 - val_loss: 0.1616 - val_categorical_accuracy: 0.9486 - 388ms/epoch - 19ms/step
Epoch 749/1000
20/20 - 0s - loss: 0.0925 - categorical_accuracy: 0.9699 - val_loss: 0.1778 - val_categorical_accuracy: 0.9431 - 397ms/epoch - 20ms/step
Epoch 750/1000
20/20 - 1s - loss: 0.2016 - categorical_accuracy: 0.9315 - val_loss: 0.1612 - val_categorical_accuracy: 0.9476 - 662ms/epoch - 33ms/step
Epoch 751/1000
20/20 - 0s - loss: 0.0945 - categorical_accuracy: 0.9697 - val_loss: 0.1641 - val_categorical_accuracy: 0.9476 - 393ms/epoch - 20ms/step
Epoch 752/1000
20/20 - 0s - loss: 0.0941 - categorical_accuracy: 0.9694 - val_loss: 0.1600 - val_categorical_accuracy: 0.9484 - 398ms/epoch - 20ms/step
Epoch 753/1000
20/20 - 0s - loss: 0.0936 - categorical_accuracy: 0.9694 - val_loss: 0.1703 - val_categorical_accuracy: 0.9442 - 416ms/epoch - 21ms/step
Epoch 754/1000
20/20 - 3s - loss: 0.4954 - categorical_accuracy: 0.8809 - val_loss: 0.2464 - val_categorical_accuracy: 0.9161 - 3s/epoch - 143ms/step
Epoch 755/1000
20/20 - 3s - loss: 0.1373 - categorical_accuracy: 0.9547 - val_loss: 0.1701 - val_categorical_accuracy: 0.9443 - 3s/epoch - 169ms/step
Epoch 756/1000
20/20 - 3s - loss: 0.1020 - categorical_accuracy: 0.9672 - val_loss: 0.1628 - val_categorical_accuracy: 0.9474 - 3s/epoch - 145ms/step
Epoch 757/1000
20/20 - 3s - loss: 0.1033 - categorical_accuracy: 0.9660 - val_loss: 0.1680 - val_categorical_accuracy: 0.9467 - 3s/epoch - 127ms/step
Epoch 758/1000
20/20 - 3s - loss: 0.0942 - categorical_accuracy: 0.9697 - val_loss: 0.1616 - val_categorical_accuracy: 0.9484 - 3s/epoch - 146ms/step
Epoch 759/1000
20/20 - 3s - loss: 0.1396 - categorical_accuracy: 0.9501 - val_loss: 0.2625 - val_categorical_accuracy: 0.9126 - 3s/epoch - 149ms/step
Epoch 760/1000
20/20 - 1s - loss: 0.1095 - categorical_accuracy: 0.9633 - val_loss: 0.1596 - val_categorical_accuracy: 0.9485 - 772ms/epoch - 39ms/step
Epoch 761/1000
20/20 - 1s - loss: 0.0952 - categorical_accuracy: 0.9689 - val_loss: 0.1748 - val_categorical_accuracy: 0.9420 - 812ms/epoch - 41ms/step
Epoch 762/1000
20/20 - 1s - loss: 0.1006 - categorical_accuracy: 0.9666 - val_loss: 0.1641 - val_categorical_accuracy: 0.9468 - 1s/epoch - 52ms/step
Epoch 763/1000
20/20 - 1s - loss: 0.1183 - categorical_accuracy: 0.9591 - val_loss: 0.1602 - val_categorical_accuracy: 0.9482 - 1s/epoch - 55ms/step
Epoch 764/1000
20/20 - 1s - loss: 0.0941 - categorical_accuracy: 0.9690 - val_loss: 0.1779 - val_categorical_accuracy: 0.9415 - 834ms/epoch - 42ms/step
Epoch 765/1000
20/20 - 1s - loss: 0.0994 - categorical_accuracy: 0.9665 - val_loss: 0.1651 - val_categorical_accuracy: 0.9473 - 599ms/epoch - 30ms/step
Epoch 766/1000
20/20 - 1s - loss: 0.1199 - categorical_accuracy: 0.9585 - val_loss: 0.5892 - val_categorical_accuracy: 0.8340 - 587ms/epoch - 29ms/step
Epoch 767/1000
20/20 - 1s - loss: 0.1601 - categorical_accuracy: 0.9523 - val_loss: 0.1609 - val_categorical_accuracy: 0.9482 - 599ms/epoch - 30ms/step
Epoch 768/1000
20/20 - 1s - loss: 0.0947 - categorical_accuracy: 0.9693 - val_loss: 0.1688 - val_categorical_accuracy: 0.9444 - 597ms/epoch - 30ms/step
Epoch 769/1000
20/20 - 1s - loss: 0.2027 - categorical_accuracy: 0.9335 - val_loss: 0.1615 - val_categorical_accuracy: 0.9472 - 584ms/epoch - 29ms/step
Epoch 770/1000
20/20 - 1s - loss: 0.0934 - categorical_accuracy: 0.9702 - val_loss: 0.1611 - val_categorical_accuracy: 0.9481 - 607ms/epoch - 30ms/step
Epoch 771/1000
20/20 - 1s - loss: 0.0959 - categorical_accuracy: 0.9684 - val_loss: 0.1639 - val_categorical_accuracy: 0.9476 - 562ms/epoch - 28ms/step
Epoch 772/1000
20/20 - 1s - loss: 0.0942 - categorical_accuracy: 0.9692 - val_loss: 0.1754 - val_categorical_accuracy: 0.9447 - 576ms/epoch - 29ms/step
Epoch 773/1000
20/20 - 1s - loss: 0.1146 - categorical_accuracy: 0.9596 - val_loss: 0.2296 - val_categorical_accuracy: 0.9272 - 572ms/epoch - 29ms/step
Epoch 774/1000
20/20 - 1s - loss: 0.1618 - categorical_accuracy: 0.9418 - val_loss: 0.1584 - val_categorical_accuracy: 0.9488 - 582ms/epoch - 29ms/step
Epoch 775/1000
20/20 - 1s - loss: 0.0903 - categorical_accuracy: 0.9711 - val_loss: 0.1609 - val_categorical_accuracy: 0.9476 - 583ms/epoch - 29ms/step
Epoch 776/1000
20/20 - 1s - loss: 0.1006 - categorical_accuracy: 0.9664 - val_loss: 0.1787 - val_categorical_accuracy: 0.9400 - 586ms/epoch - 29ms/step
Epoch 777/1000
20/20 - 1s - loss: 0.1133 - categorical_accuracy: 0.9608 - val_loss: 0.1627 - val_categorical_accuracy: 0.9477 - 597ms/epoch - 30ms/step
Epoch 778/1000
20/20 - 1s - loss: 0.0905 - categorical_accuracy: 0.9704 - val_loss: 0.1567 - val_categorical_accuracy: 0.9495 - 581ms/epoch - 29ms/step
Epoch 779/1000
20/20 - 1s - loss: 0.0992 - categorical_accuracy: 0.9667 - val_loss: 0.2144 - val_categorical_accuracy: 0.9256 - 612ms/epoch - 31ms/step
Epoch 780/1000
20/20 - 1s - loss: 0.2231 - categorical_accuracy: 0.9284 - val_loss: 0.1598 - val_categorical_accuracy: 0.9483 - 581ms/epoch - 29ms/step
Epoch 781/1000
20/20 - 1s - loss: 0.0915 - categorical_accuracy: 0.9705 - val_loss: 0.1613 - val_categorical_accuracy: 0.9475 - 565ms/epoch - 28ms/step
Epoch 782/1000
20/20 - 1s - loss: 0.1010 - categorical_accuracy: 0.9658 - val_loss: 0.1682 - val_categorical_accuracy: 0.9446 - 570ms/epoch - 28ms/step
Epoch 783/1000
20/20 - 1s - loss: 0.0912 - categorical_accuracy: 0.9702 - val_loss: 0.1634 - val_categorical_accuracy: 0.9479 - 576ms/epoch - 29ms/step
Epoch 784/1000
20/20 - 1s - loss: 0.1005 - categorical_accuracy: 0.9659 - val_loss: 0.1580 - val_categorical_accuracy: 0.9494 - 592ms/epoch - 30ms/step
Epoch 785/1000
20/20 - 1s - loss: 0.0900 - categorical_accuracy: 0.9704 - val_loss: 0.1710 - val_categorical_accuracy: 0.9433 - 568ms/epoch - 28ms/step
Epoch 786/1000
20/20 - 1s - loss: 0.1929 - categorical_accuracy: 0.9373 - val_loss: 0.1588 - val_categorical_accuracy: 0.9487 - 569ms/epoch - 28ms/step
Epoch 787/1000
20/20 - 1s - loss: 0.0894 - categorical_accuracy: 0.9712 - val_loss: 0.1611 - val_categorical_accuracy: 0.9481 - 563ms/epoch - 28ms/step
Epoch 788/1000
20/20 - 1s - loss: 0.0932 - categorical_accuracy: 0.9696 - val_loss: 0.1632 - val_categorical_accuracy: 0.9467 - 558ms/epoch - 28ms/step
Epoch 789/1000
20/20 - 1s - loss: 0.0965 - categorical_accuracy: 0.9679 - val_loss: 0.1592 - val_categorical_accuracy: 0.9488 - 583ms/epoch - 29ms/step
Epoch 790/1000
20/20 - 1s - loss: 0.1530 - categorical_accuracy: 0.9474 - val_loss: 0.7072 - val_categorical_accuracy: 0.8299 - 555ms/epoch - 28ms/step
Epoch 791/1000
20/20 - 1s - loss: 0.1328 - categorical_accuracy: 0.9587 - val_loss: 0.1692 - val_categorical_accuracy: 0.9461 - 566ms/epoch - 28ms/step
Epoch 792/1000
20/20 - 1s - loss: 0.0925 - categorical_accuracy: 0.9698 - val_loss: 0.1636 - val_categorical_accuracy: 0.9483 - 584ms/epoch - 29ms/step
Epoch 793/1000
20/20 - 1s - loss: 0.0918 - categorical_accuracy: 0.9698 - val_loss: 0.1629 - val_categorical_accuracy: 0.9482 - 595ms/epoch - 30ms/step
Epoch 794/1000
20/20 - 1s - loss: 0.0944 - categorical_accuracy: 0.9687 - val_loss: 0.1763 - val_categorical_accuracy: 0.9412 - 593ms/epoch - 30ms/step
Epoch 795/1000
20/20 - 1s - loss: 0.1054 - categorical_accuracy: 0.9645 - val_loss: 0.1595 - val_categorical_accuracy: 0.9488 - 577ms/epoch - 29ms/step
Epoch 796/1000
20/20 - 1s - loss: 0.0955 - categorical_accuracy: 0.9683 - val_loss: 0.1640 - val_categorical_accuracy: 0.9474 - 568ms/epoch - 28ms/step
Epoch 797/1000
20/20 - 1s - loss: 0.2085 - categorical_accuracy: 0.9341 - val_loss: 0.1694 - val_categorical_accuracy: 0.9450 - 571ms/epoch - 29ms/step
Epoch 798/1000
20/20 - 1s - loss: 0.0898 - categorical_accuracy: 0.9716 - val_loss: 0.1557 - val_categorical_accuracy: 0.9497 - 575ms/epoch - 29ms/step
Epoch 799/1000
20/20 - 1s - loss: 0.0919 - categorical_accuracy: 0.9694 - val_loss: 0.1980 - val_categorical_accuracy: 0.9320 - 567ms/epoch - 28ms/step
Epoch 800/1000
20/20 - 1s - loss: 0.2868 - categorical_accuracy: 0.9186 - val_loss: 0.1649 - val_categorical_accuracy: 0.9463 - 569ms/epoch - 28ms/step
Epoch 801/1000
20/20 - 1s - loss: 0.0942 - categorical_accuracy: 0.9695 - val_loss: 0.1682 - val_categorical_accuracy: 0.9470 - 584ms/epoch - 29ms/step
Epoch 802/1000
20/20 - 1s - loss: 0.0897 - categorical_accuracy: 0.9708 - val_loss: 0.1561 - val_categorical_accuracy: 0.9504 - 563ms/epoch - 28ms/step
Epoch 803/1000
20/20 - 1s - loss: 0.0870 - categorical_accuracy: 0.9720 - val_loss: 0.1561 - val_categorical_accuracy: 0.9510 - 586ms/epoch - 29ms/step
Epoch 804/1000
20/20 - 1s - loss: 0.0990 - categorical_accuracy: 0.9662 - val_loss: 0.3128 - val_categorical_accuracy: 0.9037 - 574ms/epoch - 29ms/step
Epoch 805/1000
20/20 - 1s - loss: 0.1749 - categorical_accuracy: 0.9385 - val_loss: 0.1563 - val_categorical_accuracy: 0.9496 - 662ms/epoch - 33ms/step
Epoch 806/1000
20/20 - 1s - loss: 0.0894 - categorical_accuracy: 0.9711 - val_loss: 0.1616 - val_categorical_accuracy: 0.9483 - 612ms/epoch - 31ms/step
Epoch 807/1000
20/20 - 1s - loss: 0.0900 - categorical_accuracy: 0.9706 - val_loss: 0.2157 - val_categorical_accuracy: 0.9323 - 841ms/epoch - 42ms/step
Epoch 808/1000
20/20 - 1s - loss: 0.1944 - categorical_accuracy: 0.9358 - val_loss: 0.1558 - val_categorical_accuracy: 0.9499 - 1s/epoch - 62ms/step
Epoch 809/1000
20/20 - 2s - loss: 0.0880 - categorical_accuracy: 0.9719 - val_loss: 0.1596 - val_categorical_accuracy: 0.9486 - 2s/epoch - 93ms/step
Epoch 810/1000
20/20 - 1s - loss: 0.0873 - categorical_accuracy: 0.9721 - val_loss: 0.1654 - val_categorical_accuracy: 0.9478 - 1s/epoch - 74ms/step
Epoch 811/1000
20/20 - 3s - loss: 0.0969 - categorical_accuracy: 0.9674 - val_loss: 0.1704 - val_categorical_accuracy: 0.9467 - 3s/epoch - 134ms/step
Epoch 812/1000
20/20 - 3s - loss: 0.1090 - categorical_accuracy: 0.9623 - val_loss: 0.1649 - val_categorical_accuracy: 0.9478 - 3s/epoch - 153ms/step
Epoch 813/1000
20/20 - 2s - loss: 0.0888 - categorical_accuracy: 0.9712 - val_loss: 0.1715 - val_categorical_accuracy: 0.9467 - 2s/epoch - 118ms/step
Epoch 814/1000
20/20 - 1s - loss: 0.0889 - categorical_accuracy: 0.9710 - val_loss: 0.1619 - val_categorical_accuracy: 0.9493 - 870ms/epoch - 43ms/step
Epoch 815/1000
20/20 - 1s - loss: 0.0905 - categorical_accuracy: 0.9699 - val_loss: 0.1647 - val_categorical_accuracy: 0.9484 - 685ms/epoch - 34ms/step
Epoch 816/1000
20/20 - 1s - loss: 0.1212 - categorical_accuracy: 0.9580 - val_loss: 0.4649 - val_categorical_accuracy: 0.8581 - 623ms/epoch - 31ms/step
Epoch 817/1000
20/20 - 1s - loss: 0.1496 - categorical_accuracy: 0.9525 - val_loss: 0.1572 - val_categorical_accuracy: 0.9502 - 619ms/epoch - 31ms/step
Epoch 818/1000
20/20 - 1s - loss: 0.0860 - categorical_accuracy: 0.9724 - val_loss: 0.1585 - val_categorical_accuracy: 0.9491 - 605ms/epoch - 30ms/step
Epoch 819/1000
20/20 - 1s - loss: 0.1058 - categorical_accuracy: 0.9643 - val_loss: 0.2083 - val_categorical_accuracy: 0.9282 - 636ms/epoch - 32ms/step
Epoch 820/1000
20/20 - 1s - loss: 0.2485 - categorical_accuracy: 0.9263 - val_loss: 0.1584 - val_categorical_accuracy: 0.9487 - 684ms/epoch - 34ms/step
Epoch 821/1000
20/20 - 1s - loss: 0.0872 - categorical_accuracy: 0.9723 - val_loss: 0.1563 - val_categorical_accuracy: 0.9503 - 598ms/epoch - 30ms/step
Epoch 822/1000
20/20 - 1s - loss: 0.0920 - categorical_accuracy: 0.9696 - val_loss: 0.2025 - val_categorical_accuracy: 0.9375 - 604ms/epoch - 30ms/step
Epoch 823/1000
20/20 - 1s - loss: 0.0875 - categorical_accuracy: 0.9718 - val_loss: 0.1570 - val_categorical_accuracy: 0.9496 - 629ms/epoch - 31ms/step
Epoch 824/1000
20/20 - 1s - loss: 0.0919 - categorical_accuracy: 0.9694 - val_loss: 0.1591 - val_categorical_accuracy: 0.9492 - 625ms/epoch - 31ms/step
Epoch 825/1000
20/20 - 1s - loss: 0.1002 - categorical_accuracy: 0.9667 - val_loss: 0.2011 - val_categorical_accuracy: 0.9326 - 631ms/epoch - 32ms/step
Epoch 826/1000
20/20 - 1s - loss: 0.0984 - categorical_accuracy: 0.9669 - val_loss: 0.1584 - val_categorical_accuracy: 0.9498 - 577ms/epoch - 29ms/step
Epoch 827/1000
20/20 - 1s - loss: 0.0817 - categorical_accuracy: 0.9737 - val_loss: 0.1578 - val_categorical_accuracy: 0.9501 - 599ms/epoch - 30ms/step
Epoch 828/1000
20/20 - 1s - loss: 0.0826 - categorical_accuracy: 0.9728 - val_loss: 0.1881 - val_categorical_accuracy: 0.9421 - 602ms/epoch - 30ms/step
Epoch 829/1000
20/20 - 1s - loss: 0.2551 - categorical_accuracy: 0.9132 - val_loss: 0.2210 - val_categorical_accuracy: 0.9320 - 590ms/epoch - 29ms/step
Epoch 830/1000
20/20 - 1s - loss: 0.1053 - categorical_accuracy: 0.9667 - val_loss: 0.1607 - val_categorical_accuracy: 0.9484 - 631ms/epoch - 32ms/step
Epoch 831/1000
20/20 - 1s - loss: 0.0871 - categorical_accuracy: 0.9721 - val_loss: 0.1577 - val_categorical_accuracy: 0.9507 - 645ms/epoch - 32ms/step
Epoch 832/1000
20/20 - 1s - loss: 0.0866 - categorical_accuracy: 0.9716 - val_loss: 0.1671 - val_categorical_accuracy: 0.9455 - 644ms/epoch - 32ms/step
Epoch 833/1000
20/20 - 1s - loss: 0.0851 - categorical_accuracy: 0.9724 - val_loss: 0.1632 - val_categorical_accuracy: 0.9475 - 502ms/epoch - 25ms/step
Epoch 834/1000
20/20 - 1s - loss: 0.2151 - categorical_accuracy: 0.9342 - val_loss: 0.1580 - val_categorical_accuracy: 0.9490 - 570ms/epoch - 28ms/step
Epoch 835/1000
20/20 - 1s - loss: 0.0857 - categorical_accuracy: 0.9729 - val_loss: 0.1553 - val_categorical_accuracy: 0.9507 - 553ms/epoch - 28ms/step
Epoch 836/1000
20/20 - 1s - loss: 0.0842 - categorical_accuracy: 0.9727 - val_loss: 0.1659 - val_categorical_accuracy: 0.9482 - 566ms/epoch - 28ms/step
Epoch 837/1000
20/20 - 1s - loss: 0.0847 - categorical_accuracy: 0.9720 - val_loss: 0.1606 - val_categorical_accuracy: 0.9501 - 568ms/epoch - 28ms/step
Epoch 838/1000
20/20 - 1s - loss: 0.0997 - categorical_accuracy: 0.9662 - val_loss: 0.1782 - val_categorical_accuracy: 0.9441 - 546ms/epoch - 27ms/step
Epoch 839/1000
20/20 - 1s - loss: 0.0859 - categorical_accuracy: 0.9721 - val_loss: 0.1856 - val_categorical_accuracy: 0.9425 - 740ms/epoch - 37ms/step
Epoch 840/1000
20/20 - 1s - loss: 0.1292 - categorical_accuracy: 0.9531 - val_loss: 0.3797 - val_categorical_accuracy: 0.8885 - 516ms/epoch - 26ms/step
Epoch 841/1000
20/20 - 1s - loss: 0.1292 - categorical_accuracy: 0.9553 - val_loss: 0.1546 - val_categorical_accuracy: 0.9511 - 513ms/epoch - 26ms/step
Epoch 842/1000
20/20 - 1s - loss: 0.0850 - categorical_accuracy: 0.9724 - val_loss: 0.1738 - val_categorical_accuracy: 0.9458 - 637ms/epoch - 32ms/step
Epoch 843/1000
20/20 - 1s - loss: 0.0933 - categorical_accuracy: 0.9685 - val_loss: 0.1731 - val_categorical_accuracy: 0.9461 - 645ms/epoch - 32ms/step
Epoch 844/1000
20/20 - 1s - loss: 0.0837 - categorical_accuracy: 0.9728 - val_loss: 0.1792 - val_categorical_accuracy: 0.9448 - 611ms/epoch - 31ms/step
Epoch 845/1000
20/20 - 1s - loss: 0.2005 - categorical_accuracy: 0.9343 - val_loss: 0.1568 - val_categorical_accuracy: 0.9497 - 646ms/epoch - 32ms/step
Epoch 846/1000
20/20 - 1s - loss: 0.0854 - categorical_accuracy: 0.9725 - val_loss: 0.1553 - val_categorical_accuracy: 0.9510 - 523ms/epoch - 26ms/step
Epoch 847/1000
20/20 - 1s - loss: 0.0813 - categorical_accuracy: 0.9739 - val_loss: 0.1583 - val_categorical_accuracy: 0.9495 - 575ms/epoch - 29ms/step
Epoch 848/1000
20/20 - 1s - loss: 0.0867 - categorical_accuracy: 0.9717 - val_loss: 0.1563 - val_categorical_accuracy: 0.9505 - 540ms/epoch - 27ms/step
Epoch 849/1000
20/20 - 1s - loss: 0.0846 - categorical_accuracy: 0.9722 - val_loss: 0.1613 - val_categorical_accuracy: 0.9482 - 530ms/epoch - 27ms/step
Epoch 850/1000
20/20 - 1s - loss: 0.2131 - categorical_accuracy: 0.9311 - val_loss: 0.1955 - val_categorical_accuracy: 0.9369 - 572ms/epoch - 29ms/step
Epoch 851/1000
20/20 - 1s - loss: 0.0903 - categorical_accuracy: 0.9709 - val_loss: 0.1552 - val_categorical_accuracy: 0.9510 - 775ms/epoch - 39ms/step
Epoch 852/1000
20/20 - 1s - loss: 0.0828 - categorical_accuracy: 0.9735 - val_loss: 0.1662 - val_categorical_accuracy: 0.9484 - 600ms/epoch - 30ms/step
Epoch 853/1000
20/20 - 1s - loss: 0.0840 - categorical_accuracy: 0.9725 - val_loss: 0.1782 - val_categorical_accuracy: 0.9447 - 568ms/epoch - 28ms/step
Epoch 854/1000
20/20 - 1s - loss: 0.1051 - categorical_accuracy: 0.9635 - val_loss: 0.2435 - val_categorical_accuracy: 0.9254 - 531ms/epoch - 27ms/step
Epoch 855/1000
20/20 - 0s - loss: 0.0934 - categorical_accuracy: 0.9680 - val_loss: 0.1606 - val_categorical_accuracy: 0.9492 - 473ms/epoch - 24ms/step
Epoch 856/1000
20/20 - 0s - loss: 0.0858 - categorical_accuracy: 0.9719 - val_loss: 0.1574 - val_categorical_accuracy: 0.9511 - 498ms/epoch - 25ms/step
Epoch 857/1000
20/20 - 1s - loss: 0.0850 - categorical_accuracy: 0.9723 - val_loss: 0.1584 - val_categorical_accuracy: 0.9495 - 533ms/epoch - 27ms/step
Epoch 858/1000
20/20 - 1s - loss: 0.0991 - categorical_accuracy: 0.9660 - val_loss: 0.1598 - val_categorical_accuracy: 0.9492 - 802ms/epoch - 40ms/step
Epoch 859/1000
20/20 - 0s - loss: 0.0816 - categorical_accuracy: 0.9732 - val_loss: 0.1567 - val_categorical_accuracy: 0.9507 - 446ms/epoch - 22ms/step
Epoch 860/1000
20/20 - 0s - loss: 0.1082 - categorical_accuracy: 0.9635 - val_loss: 0.6711 - val_categorical_accuracy: 0.8503 - 328ms/epoch - 16ms/step
Epoch 861/1000
20/20 - 0s - loss: 0.1865 - categorical_accuracy: 0.9427 - val_loss: 0.1584 - val_categorical_accuracy: 0.9494 - 350ms/epoch - 18ms/step
Epoch 862/1000
20/20 - 0s - loss: 0.0863 - categorical_accuracy: 0.9714 - val_loss: 0.1571 - val_categorical_accuracy: 0.9510 - 331ms/epoch - 17ms/step
Epoch 863/1000
20/20 - 0s - loss: 0.0796 - categorical_accuracy: 0.9743 - val_loss: 0.1558 - val_categorical_accuracy: 0.9513 - 318ms/epoch - 16ms/step
Epoch 864/1000
20/20 - 0s - loss: 0.0822 - categorical_accuracy: 0.9736 - val_loss: 0.1532 - val_categorical_accuracy: 0.9528 - 341ms/epoch - 17ms/step
Epoch 865/1000
20/20 - 0s - loss: 0.0776 - categorical_accuracy: 0.9750 - val_loss: 0.1598 - val_categorical_accuracy: 0.9504 - 326ms/epoch - 16ms/step
Epoch 866/1000
20/20 - 0s - loss: 1.0356 - categorical_accuracy: 0.7862 - val_loss: 0.6698 - val_categorical_accuracy: 0.7585 - 341ms/epoch - 17ms/step
Epoch 867/1000
20/20 - 0s - loss: 0.3691 - categorical_accuracy: 0.8697 - val_loss: 0.2411 - val_categorical_accuracy: 0.9187 - 354ms/epoch - 18ms/step
Epoch 868/1000
20/20 - 0s - loss: 0.1534 - categorical_accuracy: 0.9486 - val_loss: 0.1859 - val_categorical_accuracy: 0.9385 - 355ms/epoch - 18ms/step
Epoch 869/1000
20/20 - 0s - loss: 0.1136 - categorical_accuracy: 0.9629 - val_loss: 0.1715 - val_categorical_accuracy: 0.9447 - 360ms/epoch - 18ms/step
Epoch 870/1000
20/20 - 0s - loss: 0.0990 - categorical_accuracy: 0.9682 - val_loss: 0.1664 - val_categorical_accuracy: 0.9472 - 362ms/epoch - 18ms/step
Epoch 871/1000
20/20 - 0s - loss: 0.0914 - categorical_accuracy: 0.9706 - val_loss: 0.1609 - val_categorical_accuracy: 0.9488 - 329ms/epoch - 16ms/step
Epoch 872/1000
20/20 - 0s - loss: 0.0952 - categorical_accuracy: 0.9684 - val_loss: 0.1764 - val_categorical_accuracy: 0.9413 - 346ms/epoch - 17ms/step
Epoch 873/1000
20/20 - 0s - loss: 0.1795 - categorical_accuracy: 0.9373 - val_loss: 0.1585 - val_categorical_accuracy: 0.9491 - 373ms/epoch - 19ms/step
Epoch 874/1000
20/20 - 0s - loss: 0.0870 - categorical_accuracy: 0.9724 - val_loss: 0.1611 - val_categorical_accuracy: 0.9498 - 423ms/epoch - 21ms/step
Epoch 875/1000
20/20 - 0s - loss: 0.0884 - categorical_accuracy: 0.9711 - val_loss: 0.1786 - val_categorical_accuracy: 0.9448 - 358ms/epoch - 18ms/step
Epoch 876/1000
20/20 - 0s - loss: 0.0891 - categorical_accuracy: 0.9706 - val_loss: 0.1665 - val_categorical_accuracy: 0.9485 - 335ms/epoch - 17ms/step
Epoch 877/1000
20/20 - 0s - loss: 0.0824 - categorical_accuracy: 0.9730 - val_loss: 0.1584 - val_categorical_accuracy: 0.9511 - 333ms/epoch - 17ms/step
Epoch 878/1000
20/20 - 0s - loss: 0.1115 - categorical_accuracy: 0.9609 - val_loss: 0.1951 - val_categorical_accuracy: 0.9393 - 364ms/epoch - 18ms/step
Epoch 879/1000
20/20 - 0s - loss: 0.0858 - categorical_accuracy: 0.9719 - val_loss: 0.1539 - val_categorical_accuracy: 0.9519 - 338ms/epoch - 17ms/step
Epoch 880/1000
20/20 - 0s - loss: 0.0807 - categorical_accuracy: 0.9740 - val_loss: 0.1565 - val_categorical_accuracy: 0.9507 - 350ms/epoch - 18ms/step
Epoch 881/1000
20/20 - 0s - loss: 0.0867 - categorical_accuracy: 0.9716 - val_loss: 0.1784 - val_categorical_accuracy: 0.9415 - 356ms/epoch - 18ms/step
Epoch 882/1000
20/20 - 0s - loss: 0.1848 - categorical_accuracy: 0.9408 - val_loss: 0.1543 - val_categorical_accuracy: 0.9508 - 355ms/epoch - 18ms/step
Epoch 883/1000
20/20 - 0s - loss: 0.0815 - categorical_accuracy: 0.9737 - val_loss: 0.1545 - val_categorical_accuracy: 0.9518 - 407ms/epoch - 20ms/step
Epoch 884/1000
20/20 - 0s - loss: 0.0874 - categorical_accuracy: 0.9714 - val_loss: 0.1925 - val_categorical_accuracy: 0.9407 - 363ms/epoch - 18ms/step
Epoch 885/1000
20/20 - 0s - loss: 0.1003 - categorical_accuracy: 0.9652 - val_loss: 0.1721 - val_categorical_accuracy: 0.9467 - 348ms/epoch - 17ms/step
Epoch 886/1000
20/20 - 0s - loss: 0.0830 - categorical_accuracy: 0.9729 - val_loss: 0.1574 - val_categorical_accuracy: 0.9508 - 361ms/epoch - 18ms/step
Epoch 887/1000
20/20 - 0s - loss: 0.0829 - categorical_accuracy: 0.9727 - val_loss: 0.1610 - val_categorical_accuracy: 0.9489 - 353ms/epoch - 18ms/step
Epoch 888/1000
20/20 - 0s - loss: 0.0851 - categorical_accuracy: 0.9721 - val_loss: 0.1621 - val_categorical_accuracy: 0.9483 - 335ms/epoch - 17ms/step
Epoch 889/1000
20/20 - 0s - loss: 0.1069 - categorical_accuracy: 0.9625 - val_loss: 0.4058 - val_categorical_accuracy: 0.8751 - 391ms/epoch - 20ms/step
Epoch 890/1000
20/20 - 0s - loss: 0.2347 - categorical_accuracy: 0.9363 - val_loss: 0.1542 - val_categorical_accuracy: 0.9514 - 365ms/epoch - 18ms/step
Epoch 891/1000
20/20 - 0s - loss: 0.0808 - categorical_accuracy: 0.9742 - val_loss: 0.1571 - val_categorical_accuracy: 0.9506 - 365ms/epoch - 18ms/step
Epoch 892/1000
20/20 - 0s - loss: 0.0793 - categorical_accuracy: 0.9745 - val_loss: 0.1532 - val_categorical_accuracy: 0.9523 - 374ms/epoch - 19ms/step
Epoch 893/1000
20/20 - 0s - loss: 0.0773 - categorical_accuracy: 0.9752 - val_loss: 0.1530 - val_categorical_accuracy: 0.9524 - 342ms/epoch - 17ms/step
Epoch 894/1000
20/20 - 0s - loss: 0.0762 - categorical_accuracy: 0.9756 - val_loss: 0.1676 - val_categorical_accuracy: 0.9490 - 343ms/epoch - 17ms/step
Epoch 895/1000
20/20 - 0s - loss: 0.0908 - categorical_accuracy: 0.9688 - val_loss: 0.1793 - val_categorical_accuracy: 0.9453 - 360ms/epoch - 18ms/step
Epoch 896/1000
20/20 - 0s - loss: 0.0833 - categorical_accuracy: 0.9726 - val_loss: 0.1635 - val_categorical_accuracy: 0.9496 - 371ms/epoch - 19ms/step
Epoch 897/1000
20/20 - 0s - loss: 0.0985 - categorical_accuracy: 0.9659 - val_loss: 0.3881 - val_categorical_accuracy: 0.8895 - 417ms/epoch - 21ms/step
Epoch 898/1000
20/20 - 0s - loss: 0.2032 - categorical_accuracy: 0.9367 - val_loss: 0.1573 - val_categorical_accuracy: 0.9488 - 381ms/epoch - 19ms/step
Epoch 899/1000
20/20 - 0s - loss: 0.0806 - categorical_accuracy: 0.9741 - val_loss: 0.1559 - val_categorical_accuracy: 0.9513 - 359ms/epoch - 18ms/step
Epoch 900/1000
20/20 - 0s - loss: 0.0798 - categorical_accuracy: 0.9744 - val_loss: 0.1559 - val_categorical_accuracy: 0.9511 - 366ms/epoch - 18ms/step
Epoch 901/1000
20/20 - 0s - loss: 0.0874 - categorical_accuracy: 0.9709 - val_loss: 0.1746 - val_categorical_accuracy: 0.9429 - 351ms/epoch - 18ms/step
Epoch 902/1000
20/20 - 0s - loss: 0.1039 - categorical_accuracy: 0.9632 - val_loss: 0.1581 - val_categorical_accuracy: 0.9500 - 357ms/epoch - 18ms/step
Epoch 903/1000
20/20 - 0s - loss: 0.0765 - categorical_accuracy: 0.9753 - val_loss: 0.1551 - val_categorical_accuracy: 0.9520 - 374ms/epoch - 19ms/step
Epoch 904/1000
20/20 - 0s - loss: 0.0795 - categorical_accuracy: 0.9741 - val_loss: 0.1659 - val_categorical_accuracy: 0.9466 - 394ms/epoch - 20ms/step
Epoch 905/1000
20/20 - 0s - loss: 0.0881 - categorical_accuracy: 0.9704 - val_loss: 0.1584 - val_categorical_accuracy: 0.9503 - 401ms/epoch - 20ms/step
Epoch 906/1000
20/20 - 0s - loss: 0.0831 - categorical_accuracy: 0.9723 - val_loss: 0.1686 - val_categorical_accuracy: 0.9468 - 375ms/epoch - 19ms/step
Epoch 907/1000
20/20 - 0s - loss: 0.0965 - categorical_accuracy: 0.9670 - val_loss: 0.3006 - val_categorical_accuracy: 0.9019 - 386ms/epoch - 19ms/step
Epoch 908/1000
20/20 - 0s - loss: 0.1792 - categorical_accuracy: 0.9431 - val_loss: 0.1575 - val_categorical_accuracy: 0.9494 - 360ms/epoch - 18ms/step
Epoch 909/1000
20/20 - 0s - loss: 0.0788 - categorical_accuracy: 0.9747 - val_loss: 0.1535 - val_categorical_accuracy: 0.9520 - 387ms/epoch - 19ms/step
Epoch 910/1000
20/20 - 0s - loss: 0.0759 - categorical_accuracy: 0.9757 - val_loss: 0.1726 - val_categorical_accuracy: 0.9469 - 338ms/epoch - 17ms/step
Epoch 911/1000
20/20 - 0s - loss: 0.0803 - categorical_accuracy: 0.9733 - val_loss: 0.1704 - val_categorical_accuracy: 0.9493 - 355ms/epoch - 18ms/step
Epoch 912/1000
20/20 - 0s - loss: 0.0807 - categorical_accuracy: 0.9735 - val_loss: 0.1758 - val_categorical_accuracy: 0.9470 - 338ms/epoch - 17ms/step
Epoch 913/1000
20/20 - 0s - loss: 0.1153 - categorical_accuracy: 0.9606 - val_loss: 0.7826 - val_categorical_accuracy: 0.8085 - 396ms/epoch - 20ms/step
Epoch 914/1000
20/20 - 0s - loss: 0.1800 - categorical_accuracy: 0.9508 - val_loss: 0.1610 - val_categorical_accuracy: 0.9501 - 423ms/epoch - 21ms/step
Epoch 915/1000
20/20 - 0s - loss: 0.0787 - categorical_accuracy: 0.9749 - val_loss: 0.1524 - val_categorical_accuracy: 0.9529 - 318ms/epoch - 16ms/step
Epoch 916/1000
20/20 - 0s - loss: 0.0748 - categorical_accuracy: 0.9761 - val_loss: 0.1592 - val_categorical_accuracy: 0.9500 - 340ms/epoch - 17ms/step
Epoch 917/1000
20/20 - 0s - loss: 0.0813 - categorical_accuracy: 0.9732 - val_loss: 0.1684 - val_categorical_accuracy: 0.9459 - 321ms/epoch - 16ms/step
Epoch 918/1000
20/20 - 0s - loss: 0.1389 - categorical_accuracy: 0.9529 - val_loss: 0.6779 - val_categorical_accuracy: 0.8486 - 364ms/epoch - 18ms/step
Epoch 919/1000
20/20 - 0s - loss: 0.1224 - categorical_accuracy: 0.9624 - val_loss: 0.1537 - val_categorical_accuracy: 0.9515 - 326ms/epoch - 16ms/step
Epoch 920/1000
20/20 - 0s - loss: 0.0781 - categorical_accuracy: 0.9748 - val_loss: 0.1760 - val_categorical_accuracy: 0.9438 - 316ms/epoch - 16ms/step
Epoch 921/1000
20/20 - 0s - loss: 0.0874 - categorical_accuracy: 0.9708 - val_loss: 0.1538 - val_categorical_accuracy: 0.9515 - 296ms/epoch - 15ms/step
Epoch 922/1000
20/20 - 0s - loss: 0.0772 - categorical_accuracy: 0.9751 - val_loss: 0.1954 - val_categorical_accuracy: 0.9413 - 319ms/epoch - 16ms/step
Epoch 923/1000
20/20 - 0s - loss: 0.2987 - categorical_accuracy: 0.9225 - val_loss: 0.1688 - val_categorical_accuracy: 0.9465 - 305ms/epoch - 15ms/step
Epoch 924/1000
20/20 - 0s - loss: 0.0858 - categorical_accuracy: 0.9724 - val_loss: 0.1551 - val_categorical_accuracy: 0.9517 - 325ms/epoch - 16ms/step
Epoch 925/1000
20/20 - 0s - loss: 0.0783 - categorical_accuracy: 0.9747 - val_loss: 0.1585 - val_categorical_accuracy: 0.9503 - 363ms/epoch - 18ms/step
Epoch 926/1000
20/20 - 0s - loss: 0.0839 - categorical_accuracy: 0.9724 - val_loss: 0.1659 - val_categorical_accuracy: 0.9469 - 346ms/epoch - 17ms/step
Epoch 927/1000
20/20 - 0s - loss: 0.0808 - categorical_accuracy: 0.9733 - val_loss: 0.1527 - val_categorical_accuracy: 0.9534 - 403ms/epoch - 20ms/step
Epoch 928/1000
20/20 - 0s - loss: 0.0789 - categorical_accuracy: 0.9743 - val_loss: 0.1564 - val_categorical_accuracy: 0.9514 - 409ms/epoch - 20ms/step
Epoch 929/1000
20/20 - 0s - loss: 0.1275 - categorical_accuracy: 0.9575 - val_loss: 1.0475 - val_categorical_accuracy: 0.8159 - 325ms/epoch - 16ms/step
Epoch 930/1000
20/20 - 0s - loss: 0.1519 - categorical_accuracy: 0.9577 - val_loss: 0.1584 - val_categorical_accuracy: 0.9511 - 341ms/epoch - 17ms/step
Epoch 931/1000
20/20 - 0s - loss: 0.0760 - categorical_accuracy: 0.9758 - val_loss: 0.1532 - val_categorical_accuracy: 0.9524 - 342ms/epoch - 17ms/step
Epoch 932/1000
20/20 - 0s - loss: 0.0738 - categorical_accuracy: 0.9767 - val_loss: 0.1537 - val_categorical_accuracy: 0.9527 - 342ms/epoch - 17ms/step
Epoch 933/1000
20/20 - 0s - loss: 0.1373 - categorical_accuracy: 0.9531 - val_loss: 0.6432 - val_categorical_accuracy: 0.8532 - 343ms/epoch - 17ms/step
Epoch 934/1000
20/20 - 0s - loss: 0.1158 - categorical_accuracy: 0.9646 - val_loss: 0.1514 - val_categorical_accuracy: 0.9530 - 343ms/epoch - 17ms/step
Epoch 935/1000
20/20 - 0s - loss: 0.0746 - categorical_accuracy: 0.9763 - val_loss: 0.1503 - val_categorical_accuracy: 0.9532 - 342ms/epoch - 17ms/step
Epoch 936/1000
20/20 - 0s - loss: 0.0748 - categorical_accuracy: 0.9760 - val_loss: 0.1756 - val_categorical_accuracy: 0.9472 - 447ms/epoch - 22ms/step
Epoch 937/1000
20/20 - 0s - loss: 0.0774 - categorical_accuracy: 0.9751 - val_loss: 0.1740 - val_categorical_accuracy: 0.9463 - 333ms/epoch - 17ms/step
Epoch 938/1000
20/20 - 0s - loss: 0.0847 - categorical_accuracy: 0.9725 - val_loss: 0.1580 - val_categorical_accuracy: 0.9522 - 339ms/epoch - 17ms/step
Epoch 939/1000
20/20 - 0s - loss: 0.0791 - categorical_accuracy: 0.9739 - val_loss: 0.1791 - val_categorical_accuracy: 0.9464 - 341ms/epoch - 17ms/step
Epoch 940/1000
20/20 - 0s - loss: 0.1069 - categorical_accuracy: 0.9623 - val_loss: 0.1621 - val_categorical_accuracy: 0.9493 - 338ms/epoch - 17ms/step
Epoch 941/1000
20/20 - 0s - loss: 0.0778 - categorical_accuracy: 0.9750 - val_loss: 0.1517 - val_categorical_accuracy: 0.9534 - 327ms/epoch - 16ms/step
Epoch 942/1000
20/20 - 0s - loss: 0.0716 - categorical_accuracy: 0.9771 - val_loss: 0.1554 - val_categorical_accuracy: 0.9526 - 363ms/epoch - 18ms/step
Epoch 943/1000
20/20 - 0s - loss: 0.0793 - categorical_accuracy: 0.9738 - val_loss: 0.2600 - val_categorical_accuracy: 0.9148 - 359ms/epoch - 18ms/step
Epoch 944/1000
20/20 - 0s - loss: 0.2001 - categorical_accuracy: 0.9425 - val_loss: 0.1549 - val_categorical_accuracy: 0.9512 - 354ms/epoch - 18ms/step
Epoch 945/1000
20/20 - 0s - loss: 0.0744 - categorical_accuracy: 0.9763 - val_loss: 0.1520 - val_categorical_accuracy: 0.9530 - 396ms/epoch - 20ms/step
Epoch 946/1000
20/20 - 1s - loss: 0.0737 - categorical_accuracy: 0.9764 - val_loss: 0.1697 - val_categorical_accuracy: 0.9460 - 752ms/epoch - 38ms/step
Epoch 947/1000
20/20 - 1s - loss: 0.0915 - categorical_accuracy: 0.9685 - val_loss: 0.1757 - val_categorical_accuracy: 0.9428 - 550ms/epoch - 27ms/step
Epoch 948/1000
20/20 - 1s - loss: 0.2172 - categorical_accuracy: 0.9376 - val_loss: 0.1563 - val_categorical_accuracy: 0.9512 - 579ms/epoch - 29ms/step
Epoch 949/1000
20/20 - 1s - loss: 0.0760 - categorical_accuracy: 0.9758 - val_loss: 0.1521 - val_categorical_accuracy: 0.9530 - 591ms/epoch - 30ms/step
Epoch 950/1000
20/20 - 1s - loss: 0.0733 - categorical_accuracy: 0.9767 - val_loss: 0.1526 - val_categorical_accuracy: 0.9535 - 589ms/epoch - 29ms/step
Epoch 951/1000
20/20 - 1s - loss: 0.0719 - categorical_accuracy: 0.9771 - val_loss: 0.1533 - val_categorical_accuracy: 0.9533 - 565ms/epoch - 28ms/step
Epoch 952/1000
20/20 - 1s - loss: 0.0726 - categorical_accuracy: 0.9767 - val_loss: 0.1614 - val_categorical_accuracy: 0.9516 - 574ms/epoch - 29ms/step
Epoch 953/1000
20/20 - 1s - loss: 0.0748 - categorical_accuracy: 0.9756 - val_loss: 0.1600 - val_categorical_accuracy: 0.9522 - 571ms/epoch - 29ms/step
Epoch 954/1000
20/20 - 1s - loss: 0.0857 - categorical_accuracy: 0.9707 - val_loss: 0.1782 - val_categorical_accuracy: 0.9478 - 571ms/epoch - 29ms/step
Epoch 955/1000
20/20 - 1s - loss: 0.0772 - categorical_accuracy: 0.9746 - val_loss: 0.1698 - val_categorical_accuracy: 0.9495 - 527ms/epoch - 26ms/step
Epoch 956/1000
20/20 - 1s - loss: 0.0916 - categorical_accuracy: 0.9682 - val_loss: 0.1719 - val_categorical_accuracy: 0.9474 - 611ms/epoch - 31ms/step
Epoch 957/1000
20/20 - 1s - loss: 0.0748 - categorical_accuracy: 0.9755 - val_loss: 0.1585 - val_categorical_accuracy: 0.9514 - 572ms/epoch - 29ms/step
Epoch 958/1000
20/20 - 1s - loss: 0.0721 - categorical_accuracy: 0.9766 - val_loss: 0.1581 - val_categorical_accuracy: 0.9533 - 558ms/epoch - 28ms/step
Epoch 959/1000
20/20 - 1s - loss: 0.0725 - categorical_accuracy: 0.9767 - val_loss: 0.1613 - val_categorical_accuracy: 0.9504 - 565ms/epoch - 28ms/step
Epoch 960/1000
20/20 - 1s - loss: 0.1886 - categorical_accuracy: 0.9420 - val_loss: 0.1677 - val_categorical_accuracy: 0.9470 - 734ms/epoch - 37ms/step
Epoch 961/1000
20/20 - 1s - loss: 0.0773 - categorical_accuracy: 0.9753 - val_loss: 0.1505 - val_categorical_accuracy: 0.9538 - 581ms/epoch - 29ms/step
Epoch 962/1000
20/20 - 1s - loss: 0.0882 - categorical_accuracy: 0.9698 - val_loss: 0.3256 - val_categorical_accuracy: 0.9047 - 614ms/epoch - 31ms/step
Epoch 963/1000
20/20 - 1s - loss: 0.2121 - categorical_accuracy: 0.9324 - val_loss: 0.1550 - val_categorical_accuracy: 0.9521 - 571ms/epoch - 29ms/step
Epoch 964/1000
20/20 - 1s - loss: 0.0748 - categorical_accuracy: 0.9764 - val_loss: 0.1503 - val_categorical_accuracy: 0.9538 - 564ms/epoch - 28ms/step
Epoch 965/1000
20/20 - 1s - loss: 0.0715 - categorical_accuracy: 0.9772 - val_loss: 0.1521 - val_categorical_accuracy: 0.9535 - 503ms/epoch - 25ms/step
Epoch 966/1000
20/20 - 1s - loss: 0.0718 - categorical_accuracy: 0.9771 - val_loss: 0.1507 - val_categorical_accuracy: 0.9543 - 552ms/epoch - 28ms/step
Epoch 967/1000
20/20 - 1s - loss: 0.0730 - categorical_accuracy: 0.9768 - val_loss: 0.1543 - val_categorical_accuracy: 0.9531 - 574ms/epoch - 29ms/step
Epoch 968/1000
20/20 - 0s - loss: 0.0696 - categorical_accuracy: 0.9777 - val_loss: 0.1602 - val_categorical_accuracy: 0.9502 - 496ms/epoch - 25ms/step
Epoch 969/1000
20/20 - 1s - loss: 0.0749 - categorical_accuracy: 0.9756 - val_loss: 0.2471 - val_categorical_accuracy: 0.9268 - 562ms/epoch - 28ms/step
Epoch 970/1000
20/20 - 1s - loss: 0.4639 - categorical_accuracy: 0.8911 - val_loss: 0.1718 - val_categorical_accuracy: 0.9454 - 552ms/epoch - 28ms/step
Epoch 971/1000
20/20 - 1s - loss: 0.0880 - categorical_accuracy: 0.9715 - val_loss: 0.1563 - val_categorical_accuracy: 0.9519 - 581ms/epoch - 29ms/step
Epoch 972/1000
20/20 - 1s - loss: 0.0752 - categorical_accuracy: 0.9764 - val_loss: 0.1534 - val_categorical_accuracy: 0.9528 - 560ms/epoch - 28ms/step
Epoch 973/1000
20/20 - 1s - loss: 0.0760 - categorical_accuracy: 0.9756 - val_loss: 0.1549 - val_categorical_accuracy: 0.9517 - 559ms/epoch - 28ms/step
Epoch 974/1000
20/20 - 1s - loss: 0.0714 - categorical_accuracy: 0.9773 - val_loss: 0.1566 - val_categorical_accuracy: 0.9522 - 557ms/epoch - 28ms/step
Epoch 975/1000
20/20 - 1s - loss: 0.0998 - categorical_accuracy: 0.9653 - val_loss: 0.3127 - val_categorical_accuracy: 0.9007 - 529ms/epoch - 26ms/step
Epoch 976/1000
20/20 - 1s - loss: 0.1795 - categorical_accuracy: 0.9430 - val_loss: 0.1526 - val_categorical_accuracy: 0.9529 - 577ms/epoch - 29ms/step
Epoch 977/1000
20/20 - 0s - loss: 0.0729 - categorical_accuracy: 0.9771 - val_loss: 0.1588 - val_categorical_accuracy: 0.9501 - 485ms/epoch - 24ms/step
Epoch 978/1000
20/20 - 1s - loss: 0.0788 - categorical_accuracy: 0.9744 - val_loss: 0.1527 - val_categorical_accuracy: 0.9532 - 565ms/epoch - 28ms/step
Epoch 979/1000
20/20 - 1s - loss: 0.0696 - categorical_accuracy: 0.9780 - val_loss: 0.1563 - val_categorical_accuracy: 0.9516 - 555ms/epoch - 28ms/step
Epoch 980/1000
20/20 - 1s - loss: 0.0801 - categorical_accuracy: 0.9730 - val_loss: 0.1547 - val_categorical_accuracy: 0.9520 - 561ms/epoch - 28ms/step
Epoch 981/1000
20/20 - 1s - loss: 0.0701 - categorical_accuracy: 0.9777 - val_loss: 0.1533 - val_categorical_accuracy: 0.9533 - 565ms/epoch - 28ms/step
Epoch 982/1000
20/20 - 1s - loss: 0.0879 - categorical_accuracy: 0.9694 - val_loss: 0.2152 - val_categorical_accuracy: 0.9367 - 575ms/epoch - 29ms/step
Epoch 983/1000
20/20 - 1s - loss: 0.0783 - categorical_accuracy: 0.9740 - val_loss: 0.1580 - val_categorical_accuracy: 0.9533 - 519ms/epoch - 26ms/step
Epoch 984/1000
20/20 - 1s - loss: 0.0725 - categorical_accuracy: 0.9762 - val_loss: 0.1760 - val_categorical_accuracy: 0.9489 - 561ms/epoch - 28ms/step
Epoch 985/1000
20/20 - 1s - loss: 0.2062 - categorical_accuracy: 0.9423 - val_loss: 0.1540 - val_categorical_accuracy: 0.9516 - 556ms/epoch - 28ms/step
Epoch 986/1000
20/20 - 1s - loss: 0.0736 - categorical_accuracy: 0.9768 - val_loss: 0.1560 - val_categorical_accuracy: 0.9527 - 554ms/epoch - 28ms/step
Epoch 987/1000
20/20 - 1s - loss: 0.0731 - categorical_accuracy: 0.9764 - val_loss: 0.1681 - val_categorical_accuracy: 0.9495 - 531ms/epoch - 27ms/step
Epoch 988/1000
20/20 - 1s - loss: 0.1996 - categorical_accuracy: 0.9367 - val_loss: 0.1579 - val_categorical_accuracy: 0.9498 - 549ms/epoch - 27ms/step
Epoch 989/1000
20/20 - 1s - loss: 0.0765 - categorical_accuracy: 0.9759 - val_loss: 0.1506 - val_categorical_accuracy: 0.9534 - 565ms/epoch - 28ms/step
Epoch 990/1000
20/20 - 1s - loss: 0.0733 - categorical_accuracy: 0.9768 - val_loss: 0.1505 - val_categorical_accuracy: 0.9532 - 592ms/epoch - 30ms/step
Epoch 991/1000
20/20 - 1s - loss: 0.0710 - categorical_accuracy: 0.9773 - val_loss: 0.1487 - val_categorical_accuracy: 0.9551 - 535ms/epoch - 27ms/step
Epoch 992/1000
20/20 - 1s - loss: 0.0708 - categorical_accuracy: 0.9774 - val_loss: 0.1646 - val_categorical_accuracy: 0.9501 - 567ms/epoch - 28ms/step
Epoch 993/1000
20/20 - 1s - loss: 0.0725 - categorical_accuracy: 0.9766 - val_loss: 0.1945 - val_categorical_accuracy: 0.9429 - 564ms/epoch - 28ms/step
Epoch 994/1000
20/20 - 1s - loss: 0.0874 - categorical_accuracy: 0.9702 - val_loss: 0.1643 - val_categorical_accuracy: 0.9502 - 572ms/epoch - 29ms/step
Epoch 995/1000
20/20 - 1s - loss: 0.0707 - categorical_accuracy: 0.9772 - val_loss: 0.1626 - val_categorical_accuracy: 0.9508 - 561ms/epoch - 28ms/step
Epoch 996/1000
20/20 - 1s - loss: 0.0740 - categorical_accuracy: 0.9760 - val_loss: 0.1538 - val_categorical_accuracy: 0.9531 - 570ms/epoch - 28ms/step
Epoch 997/1000
20/20 - 1s - loss: 0.0714 - categorical_accuracy: 0.9767 - val_loss: 0.1555 - val_categorical_accuracy: 0.9528 - 568ms/epoch - 28ms/step
Epoch 998/1000
20/20 - 1s - loss: 0.0708 - categorical_accuracy: 0.9769 - val_loss: 0.1543 - val_categorical_accuracy: 0.9544 - 561ms/epoch - 28ms/step
Epoch 999/1000
20/20 - 1s - loss: 0.0720 - categorical_accuracy: 0.9766 - val_loss: 0.1694 - val_categorical_accuracy: 0.9502 - 571ms/epoch - 29ms/step
Epoch 1000/1000
20/20 - 1s - loss: 0.1972 - categorical_accuracy: 0.9392 - val_loss: 0.1596 - val_categorical_accuracy: 0.9494 - 564ms/epoch - 28ms/step
processing fold # 2 
Epoch 1/1000
20/20 - 1s - loss: 2.0674 - categorical_accuracy: 0.1652 - val_loss: 2.0568 - val_categorical_accuracy: 0.2126 - 1s/epoch - 70ms/step
Epoch 2/1000
20/20 - 1s - loss: 2.0469 - categorical_accuracy: 0.2640 - val_loss: 2.0344 - val_categorical_accuracy: 0.3017 - 579ms/epoch - 29ms/step
Epoch 3/1000
20/20 - 1s - loss: 2.0200 - categorical_accuracy: 0.3103 - val_loss: 2.0013 - val_categorical_accuracy: 0.3122 - 562ms/epoch - 28ms/step
Epoch 4/1000
20/20 - 0s - loss: 1.9790 - categorical_accuracy: 0.3191 - val_loss: 1.9498 - val_categorical_accuracy: 0.3201 - 434ms/epoch - 22ms/step
Epoch 5/1000
20/20 - 1s - loss: 1.9171 - categorical_accuracy: 0.3234 - val_loss: 1.8765 - val_categorical_accuracy: 0.3308 - 604ms/epoch - 30ms/step
Epoch 6/1000
20/20 - 0s - loss: 1.8391 - categorical_accuracy: 0.3359 - val_loss: 1.8202 - val_categorical_accuracy: 0.3284 - 432ms/epoch - 22ms/step
Epoch 7/1000
20/20 - 0s - loss: 1.8067 - categorical_accuracy: 0.3335 - val_loss: 1.7537 - val_categorical_accuracy: 0.3412 - 427ms/epoch - 21ms/step
Epoch 8/1000
20/20 - 0s - loss: 1.7504 - categorical_accuracy: 0.3442 - val_loss: 1.7297 - val_categorical_accuracy: 0.3421 - 431ms/epoch - 22ms/step
Epoch 9/1000
20/20 - 0s - loss: 1.6964 - categorical_accuracy: 0.3616 - val_loss: 1.6399 - val_categorical_accuracy: 0.3772 - 441ms/epoch - 22ms/step
Epoch 10/1000
20/20 - 1s - loss: 1.6383 - categorical_accuracy: 0.3955 - val_loss: 1.6412 - val_categorical_accuracy: 0.3719 - 508ms/epoch - 25ms/step
Epoch 11/1000
20/20 - 0s - loss: 1.5787 - categorical_accuracy: 0.4174 - val_loss: 1.5186 - val_categorical_accuracy: 0.4207 - 463ms/epoch - 23ms/step
Epoch 12/1000
20/20 - 1s - loss: 1.5439 - categorical_accuracy: 0.4268 - val_loss: 1.4461 - val_categorical_accuracy: 0.4475 - 561ms/epoch - 28ms/step
Epoch 13/1000
20/20 - 1s - loss: 1.4908 - categorical_accuracy: 0.4446 - val_loss: 1.4023 - val_categorical_accuracy: 0.4716 - 589ms/epoch - 29ms/step
Epoch 14/1000
20/20 - 1s - loss: 1.4621 - categorical_accuracy: 0.4557 - val_loss: 1.3895 - val_categorical_accuracy: 0.4826 - 713ms/epoch - 36ms/step
Epoch 15/1000
20/20 - 1s - loss: 1.4029 - categorical_accuracy: 0.4731 - val_loss: 1.3270 - val_categorical_accuracy: 0.5068 - 641ms/epoch - 32ms/step
Epoch 16/1000
20/20 - 1s - loss: 1.3852 - categorical_accuracy: 0.4864 - val_loss: 1.4229 - val_categorical_accuracy: 0.4733 - 567ms/epoch - 28ms/step
Epoch 17/1000
20/20 - 1s - loss: 1.3459 - categorical_accuracy: 0.4973 - val_loss: 1.3082 - val_categorical_accuracy: 0.5090 - 568ms/epoch - 28ms/step
Epoch 18/1000
20/20 - 1s - loss: 1.2973 - categorical_accuracy: 0.5149 - val_loss: 1.3075 - val_categorical_accuracy: 0.5068 - 585ms/epoch - 29ms/step
Epoch 19/1000
20/20 - 1s - loss: 1.3635 - categorical_accuracy: 0.5016 - val_loss: 1.2296 - val_categorical_accuracy: 0.5382 - 609ms/epoch - 30ms/step
Epoch 20/1000
20/20 - 1s - loss: 1.2620 - categorical_accuracy: 0.5295 - val_loss: 1.2175 - val_categorical_accuracy: 0.5487 - 571ms/epoch - 29ms/step
Epoch 21/1000
20/20 - 1s - loss: 1.2340 - categorical_accuracy: 0.5427 - val_loss: 1.2931 - val_categorical_accuracy: 0.5055 - 574ms/epoch - 29ms/step
Epoch 22/1000
20/20 - 1s - loss: 1.2642 - categorical_accuracy: 0.5340 - val_loss: 1.2478 - val_categorical_accuracy: 0.5381 - 582ms/epoch - 29ms/step
Epoch 23/1000
20/20 - 1s - loss: 1.2114 - categorical_accuracy: 0.5476 - val_loss: 1.1743 - val_categorical_accuracy: 0.5531 - 568ms/epoch - 28ms/step
Epoch 24/1000
20/20 - 1s - loss: 1.1637 - categorical_accuracy: 0.5559 - val_loss: 1.2910 - val_categorical_accuracy: 0.5309 - 593ms/epoch - 30ms/step
Epoch 25/1000
20/20 - 1s - loss: 1.2867 - categorical_accuracy: 0.5335 - val_loss: 1.1060 - val_categorical_accuracy: 0.5926 - 577ms/epoch - 29ms/step
Epoch 26/1000
20/20 - 1s - loss: 1.1432 - categorical_accuracy: 0.5744 - val_loss: 1.1874 - val_categorical_accuracy: 0.5472 - 585ms/epoch - 29ms/step
Epoch 27/1000
20/20 - 1s - loss: 1.1333 - categorical_accuracy: 0.5734 - val_loss: 1.1875 - val_categorical_accuracy: 0.5540 - 579ms/epoch - 29ms/step
Epoch 28/1000
20/20 - 1s - loss: 1.1941 - categorical_accuracy: 0.5630 - val_loss: 1.1737 - val_categorical_accuracy: 0.5875 - 581ms/epoch - 29ms/step
Epoch 29/1000
20/20 - 0s - loss: 1.1001 - categorical_accuracy: 0.5855 - val_loss: 1.1121 - val_categorical_accuracy: 0.5718 - 441ms/epoch - 22ms/step
Epoch 30/1000
20/20 - 0s - loss: 1.1049 - categorical_accuracy: 0.5853 - val_loss: 1.0449 - val_categorical_accuracy: 0.6019 - 422ms/epoch - 21ms/step
Epoch 31/1000
20/20 - 0s - loss: 1.0703 - categorical_accuracy: 0.5944 - val_loss: 1.0176 - val_categorical_accuracy: 0.6344 - 455ms/epoch - 23ms/step
Epoch 32/1000
20/20 - 0s - loss: 1.1624 - categorical_accuracy: 0.5804 - val_loss: 1.5707 - val_categorical_accuracy: 0.4452 - 455ms/epoch - 23ms/step
Epoch 33/1000
20/20 - 0s - loss: 1.0941 - categorical_accuracy: 0.6036 - val_loss: 1.0249 - val_categorical_accuracy: 0.6054 - 437ms/epoch - 22ms/step
Epoch 34/1000
20/20 - 0s - loss: 1.0550 - categorical_accuracy: 0.6067 - val_loss: 0.9652 - val_categorical_accuracy: 0.6601 - 447ms/epoch - 22ms/step
Epoch 35/1000
20/20 - 1s - loss: 0.9944 - categorical_accuracy: 0.6272 - val_loss: 1.0188 - val_categorical_accuracy: 0.6132 - 527ms/epoch - 26ms/step
Epoch 36/1000
20/20 - 0s - loss: 1.0128 - categorical_accuracy: 0.6252 - val_loss: 0.9957 - val_categorical_accuracy: 0.6199 - 412ms/epoch - 21ms/step
Epoch 37/1000
20/20 - 0s - loss: 0.9937 - categorical_accuracy: 0.6297 - val_loss: 0.9583 - val_categorical_accuracy: 0.6521 - 428ms/epoch - 21ms/step
Epoch 38/1000
20/20 - 0s - loss: 0.9887 - categorical_accuracy: 0.6307 - val_loss: 0.9747 - val_categorical_accuracy: 0.6253 - 436ms/epoch - 22ms/step
Epoch 39/1000
20/20 - 1s - loss: 0.9614 - categorical_accuracy: 0.6410 - val_loss: 0.9125 - val_categorical_accuracy: 0.6658 - 601ms/epoch - 30ms/step
Epoch 40/1000
20/20 - 1s - loss: 0.9584 - categorical_accuracy: 0.6410 - val_loss: 0.9489 - val_categorical_accuracy: 0.6554 - 573ms/epoch - 29ms/step
Epoch 41/1000
20/20 - 1s - loss: 0.9456 - categorical_accuracy: 0.6544 - val_loss: 0.9199 - val_categorical_accuracy: 0.6620 - 566ms/epoch - 28ms/step
Epoch 42/1000
20/20 - 1s - loss: 0.9142 - categorical_accuracy: 0.6603 - val_loss: 1.0193 - val_categorical_accuracy: 0.6212 - 548ms/epoch - 27ms/step
Epoch 43/1000
20/20 - 1s - loss: 0.9221 - categorical_accuracy: 0.6596 - val_loss: 0.9081 - val_categorical_accuracy: 0.6660 - 573ms/epoch - 29ms/step
Epoch 44/1000
20/20 - 1s - loss: 0.8978 - categorical_accuracy: 0.6671 - val_loss: 0.8695 - val_categorical_accuracy: 0.6840 - 580ms/epoch - 29ms/step
Epoch 45/1000
20/20 - 0s - loss: 0.8773 - categorical_accuracy: 0.6732 - val_loss: 0.8853 - val_categorical_accuracy: 0.6684 - 434ms/epoch - 22ms/step
Epoch 46/1000
20/20 - 0s - loss: 0.9744 - categorical_accuracy: 0.6434 - val_loss: 2.4753 - val_categorical_accuracy: 0.4113 - 489ms/epoch - 24ms/step
Epoch 47/1000
20/20 - 0s - loss: 0.9973 - categorical_accuracy: 0.6626 - val_loss: 0.8123 - val_categorical_accuracy: 0.7029 - 432ms/epoch - 22ms/step
Epoch 48/1000
20/20 - 1s - loss: 0.8463 - categorical_accuracy: 0.6866 - val_loss: 0.8901 - val_categorical_accuracy: 0.6671 - 582ms/epoch - 29ms/step
Epoch 49/1000
20/20 - 1s - loss: 0.8513 - categorical_accuracy: 0.6817 - val_loss: 0.8318 - val_categorical_accuracy: 0.6992 - 573ms/epoch - 29ms/step
Epoch 50/1000
20/20 - 1s - loss: 0.8276 - categorical_accuracy: 0.6921 - val_loss: 0.7927 - val_categorical_accuracy: 0.7089 - 546ms/epoch - 27ms/step
Epoch 51/1000
20/20 - 0s - loss: 0.8462 - categorical_accuracy: 0.6883 - val_loss: 0.8785 - val_categorical_accuracy: 0.6885 - 435ms/epoch - 22ms/step
Epoch 52/1000
20/20 - 0s - loss: 0.7905 - categorical_accuracy: 0.7084 - val_loss: 0.7717 - val_categorical_accuracy: 0.7138 - 433ms/epoch - 22ms/step
Epoch 53/1000
20/20 - 0s - loss: 0.7944 - categorical_accuracy: 0.7054 - val_loss: 0.7721 - val_categorical_accuracy: 0.7160 - 431ms/epoch - 22ms/step
Epoch 54/1000
20/20 - 0s - loss: 0.8383 - categorical_accuracy: 0.7035 - val_loss: 0.7663 - val_categorical_accuracy: 0.7129 - 476ms/epoch - 24ms/step
Epoch 55/1000
20/20 - 0s - loss: 0.7840 - categorical_accuracy: 0.7052 - val_loss: 0.9713 - val_categorical_accuracy: 0.6340 - 425ms/epoch - 21ms/step
Epoch 56/1000
20/20 - 0s - loss: 0.7518 - categorical_accuracy: 0.7274 - val_loss: 0.7501 - val_categorical_accuracy: 0.7247 - 438ms/epoch - 22ms/step
Epoch 57/1000
20/20 - 0s - loss: 0.7924 - categorical_accuracy: 0.7066 - val_loss: 0.7319 - val_categorical_accuracy: 0.7284 - 421ms/epoch - 21ms/step
Epoch 58/1000
20/20 - 0s - loss: 0.7618 - categorical_accuracy: 0.7173 - val_loss: 0.8724 - val_categorical_accuracy: 0.6700 - 435ms/epoch - 22ms/step
Epoch 59/1000
20/20 - 0s - loss: 0.7388 - categorical_accuracy: 0.7282 - val_loss: 0.8609 - val_categorical_accuracy: 0.6717 - 418ms/epoch - 21ms/step
Epoch 60/1000
20/20 - 0s - loss: 0.7957 - categorical_accuracy: 0.7089 - val_loss: 0.7020 - val_categorical_accuracy: 0.7475 - 434ms/epoch - 22ms/step
Epoch 61/1000
20/20 - 1s - loss: 0.7201 - categorical_accuracy: 0.7318 - val_loss: 0.8353 - val_categorical_accuracy: 0.6840 - 561ms/epoch - 28ms/step
Epoch 62/1000
20/20 - 1s - loss: 0.7353 - categorical_accuracy: 0.7292 - val_loss: 0.8160 - val_categorical_accuracy: 0.6902 - 789ms/epoch - 39ms/step
Epoch 63/1000
20/20 - 1s - loss: 0.7312 - categorical_accuracy: 0.7248 - val_loss: 0.7645 - val_categorical_accuracy: 0.7230 - 574ms/epoch - 29ms/step
Epoch 64/1000
20/20 - 1s - loss: 0.7057 - categorical_accuracy: 0.7381 - val_loss: 0.6796 - val_categorical_accuracy: 0.7478 - 565ms/epoch - 28ms/step
Epoch 65/1000
20/20 - 0s - loss: 0.7069 - categorical_accuracy: 0.7348 - val_loss: 0.6517 - val_categorical_accuracy: 0.7594 - 462ms/epoch - 23ms/step
Epoch 66/1000
20/20 - 0s - loss: 0.7671 - categorical_accuracy: 0.7233 - val_loss: 0.6495 - val_categorical_accuracy: 0.7622 - 491ms/epoch - 25ms/step
Epoch 67/1000
20/20 - 0s - loss: 0.6782 - categorical_accuracy: 0.7471 - val_loss: 0.6454 - val_categorical_accuracy: 0.7605 - 451ms/epoch - 23ms/step
Epoch 68/1000
20/20 - 0s - loss: 1.0511 - categorical_accuracy: 0.6681 - val_loss: 0.8517 - val_categorical_accuracy: 0.7074 - 432ms/epoch - 22ms/step
Epoch 69/1000
20/20 - 0s - loss: 0.6977 - categorical_accuracy: 0.7548 - val_loss: 0.6465 - val_categorical_accuracy: 0.7606 - 427ms/epoch - 21ms/step
Epoch 70/1000
20/20 - 0s - loss: 0.6655 - categorical_accuracy: 0.7521 - val_loss: 0.6412 - val_categorical_accuracy: 0.7645 - 421ms/epoch - 21ms/step
Epoch 71/1000
20/20 - 0s - loss: 0.7550 - categorical_accuracy: 0.7366 - val_loss: 0.6256 - val_categorical_accuracy: 0.7731 - 428ms/epoch - 21ms/step
Epoch 72/1000
20/20 - 0s - loss: 0.6426 - categorical_accuracy: 0.7616 - val_loss: 0.6658 - val_categorical_accuracy: 0.7513 - 425ms/epoch - 21ms/step
Epoch 73/1000
20/20 - 0s - loss: 0.6418 - categorical_accuracy: 0.7592 - val_loss: 0.6372 - val_categorical_accuracy: 0.7600 - 421ms/epoch - 21ms/step
Epoch 74/1000
20/20 - 0s - loss: 0.6884 - categorical_accuracy: 0.7514 - val_loss: 0.6427 - val_categorical_accuracy: 0.7593 - 436ms/epoch - 22ms/step
Epoch 75/1000
20/20 - 0s - loss: 0.6386 - categorical_accuracy: 0.7578 - val_loss: 0.6472 - val_categorical_accuracy: 0.7523 - 428ms/epoch - 21ms/step
Epoch 76/1000
20/20 - 0s - loss: 0.6369 - categorical_accuracy: 0.7573 - val_loss: 0.7070 - val_categorical_accuracy: 0.7307 - 432ms/epoch - 22ms/step
Epoch 77/1000
20/20 - 0s - loss: 0.7606 - categorical_accuracy: 0.7425 - val_loss: 0.6603 - val_categorical_accuracy: 0.7686 - 426ms/epoch - 21ms/step
Epoch 78/1000
20/20 - 0s - loss: 0.5957 - categorical_accuracy: 0.7819 - val_loss: 0.6272 - val_categorical_accuracy: 0.7595 - 430ms/epoch - 22ms/step
Epoch 79/1000
20/20 - 0s - loss: 0.6231 - categorical_accuracy: 0.7675 - val_loss: 0.6113 - val_categorical_accuracy: 0.7742 - 420ms/epoch - 21ms/step
Epoch 80/1000
20/20 - 0s - loss: 0.7024 - categorical_accuracy: 0.7521 - val_loss: 0.5771 - val_categorical_accuracy: 0.7878 - 498ms/epoch - 25ms/step
Epoch 81/1000
20/20 - 1s - loss: 0.5962 - categorical_accuracy: 0.7751 - val_loss: 0.6031 - val_categorical_accuracy: 0.7695 - 574ms/epoch - 29ms/step
Epoch 82/1000
20/20 - 1s - loss: 0.6152 - categorical_accuracy: 0.7674 - val_loss: 0.6155 - val_categorical_accuracy: 0.7673 - 615ms/epoch - 31ms/step
Epoch 83/1000
20/20 - 1s - loss: 0.6327 - categorical_accuracy: 0.7642 - val_loss: 1.0145 - val_categorical_accuracy: 0.6737 - 581ms/epoch - 29ms/step
Epoch 84/1000
20/20 - 1s - loss: 0.6174 - categorical_accuracy: 0.7752 - val_loss: 0.6507 - val_categorical_accuracy: 0.7515 - 576ms/epoch - 29ms/step
Epoch 85/1000
20/20 - 1s - loss: 0.5829 - categorical_accuracy: 0.7791 - val_loss: 0.6051 - val_categorical_accuracy: 0.7676 - 597ms/epoch - 30ms/step
Epoch 86/1000
20/20 - 0s - loss: 0.5854 - categorical_accuracy: 0.7774 - val_loss: 0.6273 - val_categorical_accuracy: 0.7668 - 473ms/epoch - 24ms/step
Epoch 87/1000
20/20 - 1s - loss: 0.5878 - categorical_accuracy: 0.7803 - val_loss: 0.7156 - val_categorical_accuracy: 0.7411 - 571ms/epoch - 29ms/step
Epoch 88/1000
20/20 - 0s - loss: 0.8776 - categorical_accuracy: 0.7249 - val_loss: 0.5639 - val_categorical_accuracy: 0.7950 - 445ms/epoch - 22ms/step
Epoch 89/1000
20/20 - 0s - loss: 0.5446 - categorical_accuracy: 0.8006 - val_loss: 0.5981 - val_categorical_accuracy: 0.7742 - 441ms/epoch - 22ms/step
Epoch 90/1000
20/20 - 0s - loss: 0.5837 - categorical_accuracy: 0.7801 - val_loss: 0.5646 - val_categorical_accuracy: 0.7836 - 434ms/epoch - 22ms/step
Epoch 91/1000
20/20 - 0s - loss: 0.5624 - categorical_accuracy: 0.7852 - val_loss: 0.6335 - val_categorical_accuracy: 0.7683 - 451ms/epoch - 23ms/step
Epoch 92/1000
20/20 - 0s - loss: 0.6878 - categorical_accuracy: 0.7661 - val_loss: 0.5409 - val_categorical_accuracy: 0.7991 - 427ms/epoch - 21ms/step
Epoch 93/1000
20/20 - 0s - loss: 0.5691 - categorical_accuracy: 0.7887 - val_loss: 0.5582 - val_categorical_accuracy: 0.7922 - 434ms/epoch - 22ms/step
Epoch 94/1000
20/20 - 0s - loss: 0.5560 - categorical_accuracy: 0.7870 - val_loss: 0.5666 - val_categorical_accuracy: 0.7818 - 440ms/epoch - 22ms/step
Epoch 95/1000
20/20 - 0s - loss: 0.5554 - categorical_accuracy: 0.7925 - val_loss: 0.5532 - val_categorical_accuracy: 0.7917 - 443ms/epoch - 22ms/step
Epoch 96/1000
20/20 - 1s - loss: 0.5399 - categorical_accuracy: 0.7930 - val_loss: 0.5634 - val_categorical_accuracy: 0.7830 - 556ms/epoch - 28ms/step
Epoch 97/1000
20/20 - 1s - loss: 0.5652 - categorical_accuracy: 0.7881 - val_loss: 0.5565 - val_categorical_accuracy: 0.7956 - 569ms/epoch - 28ms/step
Epoch 98/1000
20/20 - 1s - loss: 0.6375 - categorical_accuracy: 0.7839 - val_loss: 0.5235 - val_categorical_accuracy: 0.8041 - 593ms/epoch - 30ms/step
Epoch 99/1000
20/20 - 1s - loss: 0.5205 - categorical_accuracy: 0.8023 - val_loss: 0.5488 - val_categorical_accuracy: 0.7872 - 574ms/epoch - 29ms/step
Epoch 100/1000
20/20 - 1s - loss: 0.5771 - categorical_accuracy: 0.7833 - val_loss: 0.6124 - val_categorical_accuracy: 0.7782 - 588ms/epoch - 29ms/step
Epoch 101/1000
20/20 - 1s - loss: 0.5808 - categorical_accuracy: 0.7950 - val_loss: 0.5245 - val_categorical_accuracy: 0.7982 - 595ms/epoch - 30ms/step
Epoch 102/1000
20/20 - 1s - loss: 0.5188 - categorical_accuracy: 0.7992 - val_loss: 0.5132 - val_categorical_accuracy: 0.8061 - 578ms/epoch - 29ms/step
Epoch 103/1000
20/20 - 1s - loss: 0.5536 - categorical_accuracy: 0.7953 - val_loss: 0.5021 - val_categorical_accuracy: 0.8160 - 568ms/epoch - 28ms/step
Epoch 104/1000
20/20 - 1s - loss: 0.5089 - categorical_accuracy: 0.8057 - val_loss: 0.5137 - val_categorical_accuracy: 0.8038 - 552ms/epoch - 28ms/step
Epoch 105/1000
20/20 - 0s - loss: 0.5077 - categorical_accuracy: 0.8088 - val_loss: 0.5275 - val_categorical_accuracy: 0.8085 - 434ms/epoch - 22ms/step
Epoch 106/1000
20/20 - 0s - loss: 0.5278 - categorical_accuracy: 0.8001 - val_loss: 0.5407 - val_categorical_accuracy: 0.7867 - 455ms/epoch - 23ms/step
Epoch 107/1000
20/20 - 0s - loss: 0.5029 - categorical_accuracy: 0.8106 - val_loss: 0.5399 - val_categorical_accuracy: 0.7955 - 445ms/epoch - 22ms/step
Epoch 108/1000
20/20 - 0s - loss: 0.5234 - categorical_accuracy: 0.8036 - val_loss: 0.6123 - val_categorical_accuracy: 0.7750 - 447ms/epoch - 22ms/step
Epoch 109/1000
20/20 - 0s - loss: 0.6159 - categorical_accuracy: 0.7922 - val_loss: 0.4876 - val_categorical_accuracy: 0.8192 - 479ms/epoch - 24ms/step
Epoch 110/1000
20/20 - 1s - loss: 0.5114 - categorical_accuracy: 0.8117 - val_loss: 0.5104 - val_categorical_accuracy: 0.8070 - 692ms/epoch - 35ms/step
Epoch 111/1000
20/20 - 1s - loss: 0.4948 - categorical_accuracy: 0.8114 - val_loss: 0.5285 - val_categorical_accuracy: 0.7972 - 594ms/epoch - 30ms/step
Epoch 112/1000
20/20 - 1s - loss: 0.4931 - categorical_accuracy: 0.8101 - val_loss: 0.5055 - val_categorical_accuracy: 0.8040 - 598ms/epoch - 30ms/step
Epoch 113/1000
20/20 - 1s - loss: 0.5203 - categorical_accuracy: 0.8045 - val_loss: 0.4860 - val_categorical_accuracy: 0.8188 - 625ms/epoch - 31ms/step
Epoch 114/1000
20/20 - 1s - loss: 0.4802 - categorical_accuracy: 0.8183 - val_loss: 0.4994 - val_categorical_accuracy: 0.8098 - 695ms/epoch - 35ms/step
Epoch 115/1000
20/20 - 1s - loss: 0.5084 - categorical_accuracy: 0.8110 - val_loss: 0.5169 - val_categorical_accuracy: 0.8100 - 599ms/epoch - 30ms/step
Epoch 116/1000
20/20 - 1s - loss: 0.4822 - categorical_accuracy: 0.8174 - val_loss: 0.4891 - val_categorical_accuracy: 0.8127 - 561ms/epoch - 28ms/step
Epoch 117/1000
20/20 - 1s - loss: 0.4950 - categorical_accuracy: 0.8136 - val_loss: 0.6446 - val_categorical_accuracy: 0.7611 - 561ms/epoch - 28ms/step
Epoch 118/1000
20/20 - 1s - loss: 0.4910 - categorical_accuracy: 0.8160 - val_loss: 0.5181 - val_categorical_accuracy: 0.7981 - 782ms/epoch - 39ms/step
Epoch 119/1000
20/20 - 1s - loss: 1.5603 - categorical_accuracy: 0.6071 - val_loss: 0.8749 - val_categorical_accuracy: 0.7196 - 600ms/epoch - 30ms/step
Epoch 120/1000
20/20 - 1s - loss: 0.6530 - categorical_accuracy: 0.7813 - val_loss: 0.5416 - val_categorical_accuracy: 0.8088 - 1s/epoch - 56ms/step
Epoch 121/1000
20/20 - 1s - loss: 0.4938 - categorical_accuracy: 0.8245 - val_loss: 0.4847 - val_categorical_accuracy: 0.8237 - 1s/epoch - 65ms/step
Epoch 122/1000
20/20 - 1s - loss: 0.4719 - categorical_accuracy: 0.8272 - val_loss: 0.5633 - val_categorical_accuracy: 0.7858 - 676ms/epoch - 34ms/step
Epoch 123/1000
20/20 - 1s - loss: 0.5000 - categorical_accuracy: 0.8150 - val_loss: 0.6893 - val_categorical_accuracy: 0.7438 - 578ms/epoch - 29ms/step
Epoch 124/1000
20/20 - 1s - loss: 0.4823 - categorical_accuracy: 0.8223 - val_loss: 0.5376 - val_categorical_accuracy: 0.7987 - 636ms/epoch - 32ms/step
Epoch 125/1000
20/20 - 1s - loss: 0.4816 - categorical_accuracy: 0.8189 - val_loss: 0.4922 - val_categorical_accuracy: 0.8199 - 583ms/epoch - 29ms/step
Epoch 126/1000
20/20 - 1s - loss: 0.4546 - categorical_accuracy: 0.8326 - val_loss: 0.4950 - val_categorical_accuracy: 0.8096 - 581ms/epoch - 29ms/step
Epoch 127/1000
20/20 - 1s - loss: 0.4636 - categorical_accuracy: 0.8237 - val_loss: 0.5213 - val_categorical_accuracy: 0.8005 - 582ms/epoch - 29ms/step
Epoch 128/1000
20/20 - 1s - loss: 0.4682 - categorical_accuracy: 0.8268 - val_loss: 0.4673 - val_categorical_accuracy: 0.8269 - 584ms/epoch - 29ms/step
Epoch 129/1000
20/20 - 1s - loss: 0.4615 - categorical_accuracy: 0.8276 - val_loss: 0.4830 - val_categorical_accuracy: 0.8152 - 588ms/epoch - 29ms/step
Epoch 130/1000
20/20 - 1s - loss: 0.4516 - categorical_accuracy: 0.8280 - val_loss: 0.4596 - val_categorical_accuracy: 0.8266 - 581ms/epoch - 29ms/step
Epoch 131/1000
20/20 - 1s - loss: 0.4558 - categorical_accuracy: 0.8285 - val_loss: 0.5629 - val_categorical_accuracy: 0.7907 - 569ms/epoch - 28ms/step
Epoch 132/1000
20/20 - 1s - loss: 0.4873 - categorical_accuracy: 0.8183 - val_loss: 0.4470 - val_categorical_accuracy: 0.8335 - 580ms/epoch - 29ms/step
Epoch 133/1000
20/20 - 1s - loss: 0.4361 - categorical_accuracy: 0.8359 - val_loss: 0.4476 - val_categorical_accuracy: 0.8326 - 575ms/epoch - 29ms/step
Epoch 134/1000
20/20 - 1s - loss: 0.4575 - categorical_accuracy: 0.8299 - val_loss: 1.1380 - val_categorical_accuracy: 0.6872 - 574ms/epoch - 29ms/step
Epoch 135/1000
20/20 - 0s - loss: 0.8302 - categorical_accuracy: 0.7549 - val_loss: 0.4539 - val_categorical_accuracy: 0.8395 - 491ms/epoch - 25ms/step
Epoch 136/1000
20/20 - 1s - loss: 0.4171 - categorical_accuracy: 0.8520 - val_loss: 0.4287 - val_categorical_accuracy: 0.8416 - 549ms/epoch - 27ms/step
Epoch 137/1000
20/20 - 1s - loss: 0.4427 - categorical_accuracy: 0.8321 - val_loss: 0.4539 - val_categorical_accuracy: 0.8256 - 526ms/epoch - 26ms/step
Epoch 138/1000
20/20 - 1s - loss: 0.4336 - categorical_accuracy: 0.8377 - val_loss: 0.5036 - val_categorical_accuracy: 0.8139 - 602ms/epoch - 30ms/step
Epoch 139/1000
20/20 - 1s - loss: 0.4684 - categorical_accuracy: 0.8273 - val_loss: 0.4379 - val_categorical_accuracy: 0.8356 - 504ms/epoch - 25ms/step
Epoch 140/1000
20/20 - 0s - loss: 0.4308 - categorical_accuracy: 0.8367 - val_loss: 0.4269 - val_categorical_accuracy: 0.8432 - 486ms/epoch - 24ms/step
Epoch 141/1000
20/20 - 0s - loss: 0.4534 - categorical_accuracy: 0.8316 - val_loss: 0.5324 - val_categorical_accuracy: 0.8029 - 431ms/epoch - 22ms/step
Epoch 142/1000
20/20 - 0s - loss: 0.4284 - categorical_accuracy: 0.8394 - val_loss: 0.4262 - val_categorical_accuracy: 0.8379 - 434ms/epoch - 22ms/step
Epoch 143/1000
20/20 - 0s - loss: 0.4128 - categorical_accuracy: 0.8451 - val_loss: 0.4695 - val_categorical_accuracy: 0.8215 - 445ms/epoch - 22ms/step
Epoch 144/1000
20/20 - 0s - loss: 0.4412 - categorical_accuracy: 0.8323 - val_loss: 0.4381 - val_categorical_accuracy: 0.8427 - 428ms/epoch - 21ms/step
Epoch 145/1000
20/20 - 0s - loss: 0.4227 - categorical_accuracy: 0.8480 - val_loss: 0.4544 - val_categorical_accuracy: 0.8242 - 418ms/epoch - 21ms/step
Epoch 146/1000
20/20 - 0s - loss: 0.4316 - categorical_accuracy: 0.8353 - val_loss: 0.4557 - val_categorical_accuracy: 0.8242 - 429ms/epoch - 21ms/step
Epoch 147/1000
20/20 - 0s - loss: 0.4149 - categorical_accuracy: 0.8436 - val_loss: 0.4496 - val_categorical_accuracy: 0.8260 - 433ms/epoch - 22ms/step
Epoch 148/1000
20/20 - 0s - loss: 0.4079 - categorical_accuracy: 0.8467 - val_loss: 0.4562 - val_categorical_accuracy: 0.8242 - 431ms/epoch - 22ms/step
Epoch 149/1000
20/20 - 0s - loss: 0.4059 - categorical_accuracy: 0.8481 - val_loss: 0.4456 - val_categorical_accuracy: 0.8312 - 426ms/epoch - 21ms/step
Epoch 150/1000
20/20 - 0s - loss: 0.4827 - categorical_accuracy: 0.8222 - val_loss: 0.4036 - val_categorical_accuracy: 0.8551 - 436ms/epoch - 22ms/step
Epoch 151/1000
20/20 - 0s - loss: 0.3872 - categorical_accuracy: 0.8588 - val_loss: 0.4513 - val_categorical_accuracy: 0.8261 - 443ms/epoch - 22ms/step
Epoch 152/1000
20/20 - 1s - loss: 0.4017 - categorical_accuracy: 0.8481 - val_loss: 0.4220 - val_categorical_accuracy: 0.8363 - 770ms/epoch - 38ms/step
Epoch 153/1000
20/20 - 1s - loss: 0.4589 - categorical_accuracy: 0.8298 - val_loss: 0.4915 - val_categorical_accuracy: 0.8164 - 641ms/epoch - 32ms/step
Epoch 154/1000
20/20 - 1s - loss: 0.3926 - categorical_accuracy: 0.8553 - val_loss: 0.3904 - val_categorical_accuracy: 0.8562 - 673ms/epoch - 34ms/step
Epoch 155/1000
20/20 - 1s - loss: 0.4109 - categorical_accuracy: 0.8449 - val_loss: 0.4141 - val_categorical_accuracy: 0.8457 - 635ms/epoch - 32ms/step
Epoch 156/1000
20/20 - 1s - loss: 0.3869 - categorical_accuracy: 0.8582 - val_loss: 0.4561 - val_categorical_accuracy: 0.8248 - 582ms/epoch - 29ms/step
Epoch 157/1000
20/20 - 1s - loss: 0.4025 - categorical_accuracy: 0.8468 - val_loss: 0.4142 - val_categorical_accuracy: 0.8394 - 627ms/epoch - 31ms/step
Epoch 158/1000
20/20 - 1s - loss: 0.4594 - categorical_accuracy: 0.8319 - val_loss: 0.4261 - val_categorical_accuracy: 0.8481 - 620ms/epoch - 31ms/step
Epoch 159/1000
20/20 - 1s - loss: 0.3939 - categorical_accuracy: 0.8556 - val_loss: 0.4543 - val_categorical_accuracy: 0.8232 - 565ms/epoch - 28ms/step
Epoch 160/1000
20/20 - 1s - loss: 0.3826 - categorical_accuracy: 0.8566 - val_loss: 0.3858 - val_categorical_accuracy: 0.8552 - 574ms/epoch - 29ms/step
Epoch 161/1000
20/20 - 1s - loss: 0.3972 - categorical_accuracy: 0.8499 - val_loss: 0.4083 - val_categorical_accuracy: 0.8506 - 565ms/epoch - 28ms/step
Epoch 162/1000
20/20 - 1s - loss: 0.4329 - categorical_accuracy: 0.8427 - val_loss: 0.3856 - val_categorical_accuracy: 0.8588 - 575ms/epoch - 29ms/step
Epoch 163/1000
20/20 - 1s - loss: 0.3855 - categorical_accuracy: 0.8559 - val_loss: 0.4118 - val_categorical_accuracy: 0.8448 - 543ms/epoch - 27ms/step
Epoch 164/1000
20/20 - 0s - loss: 0.8547 - categorical_accuracy: 0.7791 - val_loss: 2.4419 - val_categorical_accuracy: 0.3614 - 427ms/epoch - 21ms/step
Epoch 165/1000
20/20 - 1s - loss: 0.8918 - categorical_accuracy: 0.7099 - val_loss: 0.5291 - val_categorical_accuracy: 0.8141 - 533ms/epoch - 27ms/step
Epoch 166/1000
20/20 - 1s - loss: 0.4625 - categorical_accuracy: 0.8364 - val_loss: 0.4433 - val_categorical_accuracy: 0.8411 - 669ms/epoch - 33ms/step
Epoch 167/1000
20/20 - 1s - loss: 0.3957 - categorical_accuracy: 0.8588 - val_loss: 0.4068 - val_categorical_accuracy: 0.8532 - 576ms/epoch - 29ms/step
Epoch 168/1000
20/20 - 0s - loss: 0.4148 - categorical_accuracy: 0.8491 - val_loss: 0.4442 - val_categorical_accuracy: 0.8370 - 482ms/epoch - 24ms/step
Epoch 169/1000
20/20 - 1s - loss: 0.3999 - categorical_accuracy: 0.8521 - val_loss: 0.4052 - val_categorical_accuracy: 0.8499 - 532ms/epoch - 27ms/step
Epoch 170/1000
20/20 - 1s - loss: 0.3761 - categorical_accuracy: 0.8607 - val_loss: 0.4515 - val_categorical_accuracy: 0.8275 - 554ms/epoch - 28ms/step
Epoch 171/1000
20/20 - 1s - loss: 0.3756 - categorical_accuracy: 0.8605 - val_loss: 0.4178 - val_categorical_accuracy: 0.8476 - 562ms/epoch - 28ms/step
Epoch 172/1000
20/20 - 1s - loss: 0.4404 - categorical_accuracy: 0.8408 - val_loss: 0.4047 - val_categorical_accuracy: 0.8497 - 543ms/epoch - 27ms/step
Epoch 173/1000
20/20 - 1s - loss: 0.3776 - categorical_accuracy: 0.8589 - val_loss: 0.3746 - val_categorical_accuracy: 0.8626 - 509ms/epoch - 25ms/step
Epoch 174/1000
20/20 - 1s - loss: 0.3741 - categorical_accuracy: 0.8602 - val_loss: 0.3741 - val_categorical_accuracy: 0.8635 - 553ms/epoch - 28ms/step
Epoch 175/1000
20/20 - 0s - loss: 0.3629 - categorical_accuracy: 0.8637 - val_loss: 0.3695 - val_categorical_accuracy: 0.8643 - 463ms/epoch - 23ms/step
Epoch 176/1000
20/20 - 0s - loss: 0.3734 - categorical_accuracy: 0.8609 - val_loss: 0.4413 - val_categorical_accuracy: 0.8425 - 480ms/epoch - 24ms/step
Epoch 177/1000
20/20 - 0s - loss: 0.4257 - categorical_accuracy: 0.8485 - val_loss: 0.4044 - val_categorical_accuracy: 0.8466 - 489ms/epoch - 24ms/step
Epoch 178/1000
20/20 - 0s - loss: 0.3718 - categorical_accuracy: 0.8613 - val_loss: 0.3692 - val_categorical_accuracy: 0.8637 - 459ms/epoch - 23ms/step
Epoch 179/1000
20/20 - 1s - loss: 0.3616 - categorical_accuracy: 0.8656 - val_loss: 0.3656 - val_categorical_accuracy: 0.8649 - 531ms/epoch - 27ms/step
Epoch 180/1000
20/20 - 0s - loss: 0.3577 - categorical_accuracy: 0.8658 - val_loss: 0.3553 - val_categorical_accuracy: 0.8712 - 459ms/epoch - 23ms/step
Epoch 181/1000
20/20 - 1s - loss: 0.3669 - categorical_accuracy: 0.8639 - val_loss: 0.3729 - val_categorical_accuracy: 0.8621 - 503ms/epoch - 25ms/step
Epoch 182/1000
20/20 - 0s - loss: 0.4070 - categorical_accuracy: 0.8507 - val_loss: 0.4191 - val_categorical_accuracy: 0.8475 - 460ms/epoch - 23ms/step
Epoch 183/1000
20/20 - 0s - loss: 0.3577 - categorical_accuracy: 0.8693 - val_loss: 0.3929 - val_categorical_accuracy: 0.8484 - 490ms/epoch - 25ms/step
Epoch 184/1000
20/20 - 1s - loss: 0.3390 - categorical_accuracy: 0.8741 - val_loss: 0.3599 - val_categorical_accuracy: 0.8656 - 561ms/epoch - 28ms/step
Epoch 185/1000
20/20 - 1s - loss: 0.3673 - categorical_accuracy: 0.8621 - val_loss: 0.3621 - val_categorical_accuracy: 0.8653 - 929ms/epoch - 46ms/step
Epoch 186/1000
20/20 - 2s - loss: 0.3725 - categorical_accuracy: 0.8622 - val_loss: 0.3757 - val_categorical_accuracy: 0.8638 - 2s/epoch - 80ms/step
Epoch 187/1000
20/20 - 0s - loss: 0.4206 - categorical_accuracy: 0.8503 - val_loss: 0.3477 - val_categorical_accuracy: 0.8742 - 477ms/epoch - 24ms/step
Epoch 188/1000
20/20 - 0s - loss: 0.3520 - categorical_accuracy: 0.8687 - val_loss: 0.3667 - val_categorical_accuracy: 0.8628 - 432ms/epoch - 22ms/step
Epoch 189/1000
20/20 - 0s - loss: 0.3490 - categorical_accuracy: 0.8698 - val_loss: 0.3792 - val_categorical_accuracy: 0.8626 - 444ms/epoch - 22ms/step
Epoch 190/1000
20/20 - 0s - loss: 0.3453 - categorical_accuracy: 0.8756 - val_loss: 0.3509 - val_categorical_accuracy: 0.8684 - 456ms/epoch - 23ms/step
Epoch 191/1000
20/20 - 0s - loss: 0.3543 - categorical_accuracy: 0.8654 - val_loss: 0.3520 - val_categorical_accuracy: 0.8703 - 443ms/epoch - 22ms/step
Epoch 192/1000
20/20 - 0s - loss: 0.3459 - categorical_accuracy: 0.8716 - val_loss: 0.3750 - val_categorical_accuracy: 0.8638 - 462ms/epoch - 23ms/step
Epoch 193/1000
20/20 - 0s - loss: 0.3645 - categorical_accuracy: 0.8683 - val_loss: 0.3796 - val_categorical_accuracy: 0.8585 - 438ms/epoch - 22ms/step
Epoch 194/1000
20/20 - 0s - loss: 0.3537 - categorical_accuracy: 0.8667 - val_loss: 0.3803 - val_categorical_accuracy: 0.8569 - 422ms/epoch - 21ms/step
Epoch 195/1000
20/20 - 0s - loss: 0.3399 - categorical_accuracy: 0.8749 - val_loss: 0.3547 - val_categorical_accuracy: 0.8696 - 437ms/epoch - 22ms/step
Epoch 196/1000
20/20 - 0s - loss: 0.3536 - categorical_accuracy: 0.8680 - val_loss: 0.3469 - val_categorical_accuracy: 0.8726 - 430ms/epoch - 22ms/step
Epoch 197/1000
20/20 - 0s - loss: 0.3390 - categorical_accuracy: 0.8726 - val_loss: 0.3439 - val_categorical_accuracy: 0.8735 - 448ms/epoch - 22ms/step
Epoch 198/1000
20/20 - 0s - loss: 0.3283 - categorical_accuracy: 0.8796 - val_loss: 0.3622 - val_categorical_accuracy: 0.8691 - 436ms/epoch - 22ms/step
Epoch 199/1000
20/20 - 0s - loss: 0.4459 - categorical_accuracy: 0.8546 - val_loss: 0.3394 - val_categorical_accuracy: 0.8774 - 439ms/epoch - 22ms/step
Epoch 200/1000
20/20 - 0s - loss: 0.3309 - categorical_accuracy: 0.8767 - val_loss: 0.3562 - val_categorical_accuracy: 0.8664 - 422ms/epoch - 21ms/step
Epoch 201/1000
20/20 - 0s - loss: 0.3531 - categorical_accuracy: 0.8686 - val_loss: 0.3429 - val_categorical_accuracy: 0.8755 - 434ms/epoch - 22ms/step
Epoch 202/1000
20/20 - 0s - loss: 0.3296 - categorical_accuracy: 0.8798 - val_loss: 0.3322 - val_categorical_accuracy: 0.8800 - 426ms/epoch - 21ms/step
Epoch 203/1000
20/20 - 2s - loss: 0.3404 - categorical_accuracy: 0.8758 - val_loss: 0.4221 - val_categorical_accuracy: 0.8519 - 2s/epoch - 104ms/step
Epoch 204/1000
20/20 - 1s - loss: 0.6000 - categorical_accuracy: 0.8215 - val_loss: 0.3424 - val_categorical_accuracy: 0.8798 - 1s/epoch - 63ms/step
Epoch 205/1000
20/20 - 1s - loss: 0.3082 - categorical_accuracy: 0.8923 - val_loss: 0.3367 - val_categorical_accuracy: 0.8778 - 1s/epoch - 56ms/step
Epoch 206/1000
20/20 - 1s - loss: 0.3430 - categorical_accuracy: 0.8725 - val_loss: 0.3742 - val_categorical_accuracy: 0.8611 - 629ms/epoch - 31ms/step
Epoch 207/1000
20/20 - 2s - loss: 0.3178 - categorical_accuracy: 0.8830 - val_loss: 0.3672 - val_categorical_accuracy: 0.8640 - 2s/epoch - 97ms/step
Epoch 208/1000
20/20 - 1s - loss: 0.3299 - categorical_accuracy: 0.8804 - val_loss: 0.3321 - val_categorical_accuracy: 0.8797 - 673ms/epoch - 34ms/step
Epoch 209/1000
20/20 - 1s - loss: 0.4225 - categorical_accuracy: 0.8503 - val_loss: 0.3331 - val_categorical_accuracy: 0.8820 - 609ms/epoch - 30ms/step
Epoch 210/1000
20/20 - 1s - loss: 0.3065 - categorical_accuracy: 0.8910 - val_loss: 0.3434 - val_categorical_accuracy: 0.8708 - 570ms/epoch - 28ms/step
Epoch 211/1000
20/20 - 1s - loss: 0.3192 - categorical_accuracy: 0.8806 - val_loss: 0.3467 - val_categorical_accuracy: 0.8674 - 568ms/epoch - 28ms/step
Epoch 212/1000
20/20 - 1s - loss: 0.3458 - categorical_accuracy: 0.8693 - val_loss: 0.3642 - val_categorical_accuracy: 0.8644 - 567ms/epoch - 28ms/step
Epoch 213/1000
20/20 - 1s - loss: 0.3058 - categorical_accuracy: 0.8894 - val_loss: 0.3310 - val_categorical_accuracy: 0.8801 - 606ms/epoch - 30ms/step
Epoch 214/1000
20/20 - 1s - loss: 0.3180 - categorical_accuracy: 0.8826 - val_loss: 0.3726 - val_categorical_accuracy: 0.8597 - 564ms/epoch - 28ms/step
Epoch 215/1000
20/20 - 1s - loss: 0.3277 - categorical_accuracy: 0.8777 - val_loss: 0.3282 - val_categorical_accuracy: 0.8806 - 589ms/epoch - 29ms/step
Epoch 216/1000
20/20 - 1s - loss: 0.3098 - categorical_accuracy: 0.8870 - val_loss: 0.3564 - val_categorical_accuracy: 0.8692 - 563ms/epoch - 28ms/step
Epoch 217/1000
20/20 - 1s - loss: 0.3992 - categorical_accuracy: 0.8646 - val_loss: 0.3156 - val_categorical_accuracy: 0.8881 - 571ms/epoch - 29ms/step
Epoch 218/1000
20/20 - 1s - loss: 0.3005 - categorical_accuracy: 0.8911 - val_loss: 0.3817 - val_categorical_accuracy: 0.8488 - 562ms/epoch - 28ms/step
Epoch 219/1000
20/20 - 1s - loss: 0.3376 - categorical_accuracy: 0.8739 - val_loss: 0.3576 - val_categorical_accuracy: 0.8683 - 561ms/epoch - 28ms/step
Epoch 220/1000
20/20 - 1s - loss: 0.3037 - categorical_accuracy: 0.8884 - val_loss: 0.3509 - val_categorical_accuracy: 0.8710 - 561ms/epoch - 28ms/step
Epoch 221/1000
20/20 - 1s - loss: 0.3297 - categorical_accuracy: 0.8755 - val_loss: 0.3238 - val_categorical_accuracy: 0.8837 - 608ms/epoch - 30ms/step
Epoch 222/1000
20/20 - 1s - loss: 0.3087 - categorical_accuracy: 0.8890 - val_loss: 0.3153 - val_categorical_accuracy: 0.8859 - 555ms/epoch - 28ms/step
Epoch 223/1000
20/20 - 1s - loss: 0.3073 - categorical_accuracy: 0.8873 - val_loss: 0.3375 - val_categorical_accuracy: 0.8762 - 563ms/epoch - 28ms/step
Epoch 224/1000
20/20 - 1s - loss: 0.3060 - categorical_accuracy: 0.8866 - val_loss: 0.4335 - val_categorical_accuracy: 0.8341 - 564ms/epoch - 28ms/step
Epoch 225/1000
20/20 - 1s - loss: 0.3324 - categorical_accuracy: 0.8786 - val_loss: 0.3192 - val_categorical_accuracy: 0.8871 - 564ms/epoch - 28ms/step
Epoch 226/1000
20/20 - 1s - loss: 0.3272 - categorical_accuracy: 0.8823 - val_loss: 0.3028 - val_categorical_accuracy: 0.8916 - 575ms/epoch - 29ms/step
Epoch 227/1000
20/20 - 1s - loss: 0.3022 - categorical_accuracy: 0.8878 - val_loss: 0.3229 - val_categorical_accuracy: 0.8810 - 557ms/epoch - 28ms/step
Epoch 228/1000
20/20 - 1s - loss: 0.2903 - categorical_accuracy: 0.8928 - val_loss: 0.3294 - val_categorical_accuracy: 0.8798 - 555ms/epoch - 28ms/step
Epoch 229/1000
20/20 - 1s - loss: 0.3285 - categorical_accuracy: 0.8765 - val_loss: 0.3322 - val_categorical_accuracy: 0.8775 - 577ms/epoch - 29ms/step
Epoch 230/1000
20/20 - 1s - loss: 0.2810 - categorical_accuracy: 0.9005 - val_loss: 0.3825 - val_categorical_accuracy: 0.8633 - 564ms/epoch - 28ms/step
Epoch 231/1000
20/20 - 1s - loss: 1.7980 - categorical_accuracy: 0.5716 - val_loss: 0.8583 - val_categorical_accuracy: 0.7067 - 693ms/epoch - 35ms/step
Epoch 232/1000
20/20 - 1s - loss: 0.5808 - categorical_accuracy: 0.8027 - val_loss: 0.4461 - val_categorical_accuracy: 0.8439 - 858ms/epoch - 43ms/step
Epoch 233/1000
20/20 - 1s - loss: 0.3782 - categorical_accuracy: 0.8688 - val_loss: 0.3656 - val_categorical_accuracy: 0.8713 - 844ms/epoch - 42ms/step
Epoch 234/1000
20/20 - 1s - loss: 0.3328 - categorical_accuracy: 0.8820 - val_loss: 0.3541 - val_categorical_accuracy: 0.8713 - 746ms/epoch - 37ms/step
Epoch 235/1000
20/20 - 1s - loss: 0.3210 - categorical_accuracy: 0.8843 - val_loss: 0.3413 - val_categorical_accuracy: 0.8761 - 574ms/epoch - 29ms/step
Epoch 236/1000
20/20 - 1s - loss: 0.3076 - categorical_accuracy: 0.8890 - val_loss: 0.3542 - val_categorical_accuracy: 0.8702 - 578ms/epoch - 29ms/step
Epoch 237/1000
20/20 - 1s - loss: 0.3088 - categorical_accuracy: 0.8881 - val_loss: 0.3261 - val_categorical_accuracy: 0.8816 - 604ms/epoch - 30ms/step
Epoch 238/1000
20/20 - 1s - loss: 0.3296 - categorical_accuracy: 0.8777 - val_loss: 0.3265 - val_categorical_accuracy: 0.8809 - 597ms/epoch - 30ms/step
Epoch 239/1000
20/20 - 1s - loss: 0.2932 - categorical_accuracy: 0.8927 - val_loss: 0.3487 - val_categorical_accuracy: 0.8727 - 578ms/epoch - 29ms/step
Epoch 240/1000
20/20 - 1s - loss: 0.3084 - categorical_accuracy: 0.8857 - val_loss: 0.3188 - val_categorical_accuracy: 0.8852 - 605ms/epoch - 30ms/step
Epoch 241/1000
20/20 - 1s - loss: 0.3037 - categorical_accuracy: 0.8901 - val_loss: 0.3363 - val_categorical_accuracy: 0.8776 - 745ms/epoch - 37ms/step
Epoch 242/1000
20/20 - 3s - loss: 0.2968 - categorical_accuracy: 0.8906 - val_loss: 0.3288 - val_categorical_accuracy: 0.8780 - 3s/epoch - 165ms/step
Epoch 243/1000
20/20 - 1s - loss: 0.2924 - categorical_accuracy: 0.8934 - val_loss: 0.3318 - val_categorical_accuracy: 0.8785 - 578ms/epoch - 29ms/step
Epoch 244/1000
20/20 - 1s - loss: 0.2971 - categorical_accuracy: 0.8902 - val_loss: 0.3506 - val_categorical_accuracy: 0.8722 - 559ms/epoch - 28ms/step
Epoch 245/1000
20/20 - 1s - loss: 0.3956 - categorical_accuracy: 0.8686 - val_loss: 0.2893 - val_categorical_accuracy: 0.8981 - 572ms/epoch - 29ms/step
Epoch 246/1000
20/20 - 1s - loss: 0.2672 - categorical_accuracy: 0.9051 - val_loss: 0.3347 - val_categorical_accuracy: 0.8745 - 1s/epoch - 62ms/step
Epoch 247/1000
20/20 - 2s - loss: 0.3071 - categorical_accuracy: 0.8855 - val_loss: 0.3273 - val_categorical_accuracy: 0.8802 - 2s/epoch - 76ms/step
Epoch 248/1000
20/20 - 2s - loss: 0.2999 - categorical_accuracy: 0.8902 - val_loss: 0.2967 - val_categorical_accuracy: 0.8940 - 2s/epoch - 108ms/step
Epoch 249/1000
20/20 - 1s - loss: 0.2852 - categorical_accuracy: 0.8953 - val_loss: 0.3103 - val_categorical_accuracy: 0.8855 - 1s/epoch - 58ms/step
Epoch 250/1000
20/20 - 1s - loss: 0.2780 - categorical_accuracy: 0.8981 - val_loss: 0.3131 - val_categorical_accuracy: 0.8828 - 616ms/epoch - 31ms/step
Epoch 251/1000
20/20 - 1s - loss: 0.3832 - categorical_accuracy: 0.8672 - val_loss: 0.3240 - val_categorical_accuracy: 0.8842 - 556ms/epoch - 28ms/step
Epoch 252/1000
20/20 - 1s - loss: 0.2645 - categorical_accuracy: 0.9074 - val_loss: 0.2822 - val_categorical_accuracy: 0.8995 - 632ms/epoch - 32ms/step
Epoch 253/1000
20/20 - 1s - loss: 0.2890 - categorical_accuracy: 0.8931 - val_loss: 0.3882 - val_categorical_accuracy: 0.8548 - 590ms/epoch - 29ms/step
Epoch 254/1000
20/20 - 1s - loss: 0.2977 - categorical_accuracy: 0.8918 - val_loss: 0.2889 - val_categorical_accuracy: 0.8979 - 571ms/epoch - 29ms/step
Epoch 255/1000
20/20 - 1s - loss: 0.2945 - categorical_accuracy: 0.8931 - val_loss: 0.3069 - val_categorical_accuracy: 0.8892 - 581ms/epoch - 29ms/step
Epoch 256/1000
20/20 - 1s - loss: 0.2845 - categorical_accuracy: 0.8943 - val_loss: 0.2840 - val_categorical_accuracy: 0.8982 - 630ms/epoch - 31ms/step
Epoch 257/1000
20/20 - 1s - loss: 0.2729 - categorical_accuracy: 0.9008 - val_loss: 0.3878 - val_categorical_accuracy: 0.8604 - 557ms/epoch - 28ms/step
Epoch 258/1000
20/20 - 1s - loss: 0.4348 - categorical_accuracy: 0.8686 - val_loss: 0.2872 - val_categorical_accuracy: 0.8982 - 974ms/epoch - 49ms/step
Epoch 259/1000
20/20 - 1s - loss: 0.2709 - categorical_accuracy: 0.9033 - val_loss: 0.3446 - val_categorical_accuracy: 0.8732 - 1s/epoch - 70ms/step
Epoch 260/1000
20/20 - 1s - loss: 0.2670 - categorical_accuracy: 0.9043 - val_loss: 0.2989 - val_categorical_accuracy: 0.8922 - 587ms/epoch - 29ms/step
Epoch 261/1000
20/20 - 1s - loss: 0.2967 - categorical_accuracy: 0.8906 - val_loss: 0.2984 - val_categorical_accuracy: 0.8927 - 580ms/epoch - 29ms/step
Epoch 262/1000
20/20 - 1s - loss: 0.2812 - categorical_accuracy: 0.8979 - val_loss: 0.3064 - val_categorical_accuracy: 0.8856 - 580ms/epoch - 29ms/step
Epoch 263/1000
20/20 - 1s - loss: 0.2684 - categorical_accuracy: 0.9028 - val_loss: 0.2953 - val_categorical_accuracy: 0.8921 - 571ms/epoch - 29ms/step
Epoch 264/1000
20/20 - 2s - loss: 0.2923 - categorical_accuracy: 0.8914 - val_loss: 0.3611 - val_categorical_accuracy: 0.8679 - 2s/epoch - 87ms/step
Epoch 265/1000
20/20 - 2s - loss: 0.3737 - categorical_accuracy: 0.8757 - val_loss: 0.2717 - val_categorical_accuracy: 0.9045 - 2s/epoch - 90ms/step
Epoch 266/1000
20/20 - 1s - loss: 0.2634 - categorical_accuracy: 0.9046 - val_loss: 0.2866 - val_categorical_accuracy: 0.8972 - 602ms/epoch - 30ms/step
Epoch 267/1000
20/20 - 1s - loss: 0.2756 - categorical_accuracy: 0.8999 - val_loss: 0.3999 - val_categorical_accuracy: 0.8535 - 608ms/epoch - 30ms/step
Epoch 268/1000
20/20 - 1s - loss: 0.2723 - categorical_accuracy: 0.9019 - val_loss: 0.2989 - val_categorical_accuracy: 0.8874 - 572ms/epoch - 29ms/step
Epoch 269/1000
20/20 - 1s - loss: 0.2796 - categorical_accuracy: 0.8961 - val_loss: 0.3070 - val_categorical_accuracy: 0.8874 - 572ms/epoch - 29ms/step
Epoch 270/1000
20/20 - 1s - loss: 0.2519 - categorical_accuracy: 0.9094 - val_loss: 0.2839 - val_categorical_accuracy: 0.8968 - 585ms/epoch - 29ms/step
Epoch 271/1000
20/20 - 1s - loss: 0.2863 - categorical_accuracy: 0.8953 - val_loss: 0.3361 - val_categorical_accuracy: 0.8771 - 553ms/epoch - 28ms/step
Epoch 272/1000
20/20 - 1s - loss: 0.2597 - categorical_accuracy: 0.9062 - val_loss: 0.2930 - val_categorical_accuracy: 0.8939 - 575ms/epoch - 29ms/step
Epoch 273/1000
20/20 - 1s - loss: 0.2572 - categorical_accuracy: 0.9086 - val_loss: 0.3521 - val_categorical_accuracy: 0.8691 - 567ms/epoch - 28ms/step
Epoch 274/1000
20/20 - 1s - loss: 0.2991 - categorical_accuracy: 0.8891 - val_loss: 0.3960 - val_categorical_accuracy: 0.8551 - 551ms/epoch - 28ms/step
Epoch 275/1000
20/20 - 1s - loss: 0.3747 - categorical_accuracy: 0.8740 - val_loss: 0.2739 - val_categorical_accuracy: 0.9040 - 528ms/epoch - 26ms/step
Epoch 276/1000
20/20 - 1s - loss: 0.2428 - categorical_accuracy: 0.9138 - val_loss: 0.3213 - val_categorical_accuracy: 0.8816 - 549ms/epoch - 27ms/step
Epoch 277/1000
20/20 - 1s - loss: 0.2892 - categorical_accuracy: 0.8924 - val_loss: 0.3360 - val_categorical_accuracy: 0.8758 - 520ms/epoch - 26ms/step
Epoch 278/1000
20/20 - 1s - loss: 0.2528 - categorical_accuracy: 0.9091 - val_loss: 0.2757 - val_categorical_accuracy: 0.8989 - 825ms/epoch - 41ms/step
Epoch 279/1000
20/20 - 1s - loss: 0.2698 - categorical_accuracy: 0.8995 - val_loss: 0.3267 - val_categorical_accuracy: 0.8777 - 676ms/epoch - 34ms/step
Epoch 280/1000
20/20 - 1s - loss: 0.2714 - categorical_accuracy: 0.9005 - val_loss: 0.2779 - val_categorical_accuracy: 0.8999 - 572ms/epoch - 29ms/step
Epoch 281/1000
20/20 - 1s - loss: 0.2380 - categorical_accuracy: 0.9163 - val_loss: 0.2662 - val_categorical_accuracy: 0.9049 - 565ms/epoch - 28ms/step
Epoch 282/1000
20/20 - 1s - loss: 0.2748 - categorical_accuracy: 0.8989 - val_loss: 0.2748 - val_categorical_accuracy: 0.9014 - 556ms/epoch - 28ms/step
Epoch 283/1000
20/20 - 1s - loss: 0.2702 - categorical_accuracy: 0.9017 - val_loss: 0.2696 - val_categorical_accuracy: 0.9039 - 545ms/epoch - 27ms/step
Epoch 284/1000
20/20 - 1s - loss: 0.2453 - categorical_accuracy: 0.9113 - val_loss: 0.2864 - val_categorical_accuracy: 0.8959 - 560ms/epoch - 28ms/step
Epoch 285/1000
20/20 - 1s - loss: 0.2549 - categorical_accuracy: 0.9066 - val_loss: 0.2632 - val_categorical_accuracy: 0.9064 - 561ms/epoch - 28ms/step
Epoch 286/1000
20/20 - 1s - loss: 0.5426 - categorical_accuracy: 0.8405 - val_loss: 1.4801 - val_categorical_accuracy: 0.5985 - 573ms/epoch - 29ms/step
Epoch 287/1000
20/20 - 1s - loss: 0.5546 - categorical_accuracy: 0.8285 - val_loss: 0.2954 - val_categorical_accuracy: 0.8995 - 568ms/epoch - 28ms/step
Epoch 288/1000
20/20 - 1s - loss: 0.2492 - categorical_accuracy: 0.9158 - val_loss: 0.2688 - val_categorical_accuracy: 0.9062 - 553ms/epoch - 28ms/step
Epoch 289/1000
20/20 - 1s - loss: 0.2362 - categorical_accuracy: 0.9188 - val_loss: 0.2691 - val_categorical_accuracy: 0.9055 - 566ms/epoch - 28ms/step
Epoch 290/1000
20/20 - 1s - loss: 0.2450 - categorical_accuracy: 0.9134 - val_loss: 0.3627 - val_categorical_accuracy: 0.8668 - 570ms/epoch - 28ms/step
Epoch 291/1000
20/20 - 1s - loss: 0.2665 - categorical_accuracy: 0.9021 - val_loss: 0.2925 - val_categorical_accuracy: 0.8939 - 564ms/epoch - 28ms/step
Epoch 292/1000
20/20 - 1s - loss: 0.2516 - categorical_accuracy: 0.9077 - val_loss: 0.2676 - val_categorical_accuracy: 0.9049 - 557ms/epoch - 28ms/step
Epoch 293/1000
20/20 - 1s - loss: 0.2431 - categorical_accuracy: 0.9127 - val_loss: 0.3471 - val_categorical_accuracy: 0.8744 - 569ms/epoch - 28ms/step
Epoch 294/1000
20/20 - 1s - loss: 0.2581 - categorical_accuracy: 0.9075 - val_loss: 0.2606 - val_categorical_accuracy: 0.9080 - 568ms/epoch - 28ms/step
Epoch 295/1000
20/20 - 1s - loss: 0.3125 - categorical_accuracy: 0.8904 - val_loss: 0.3417 - val_categorical_accuracy: 0.8782 - 571ms/epoch - 29ms/step
Epoch 296/1000
20/20 - 1s - loss: 0.2683 - categorical_accuracy: 0.9013 - val_loss: 0.2650 - val_categorical_accuracy: 0.9046 - 561ms/epoch - 28ms/step
Epoch 297/1000
20/20 - 1s - loss: 0.2369 - categorical_accuracy: 0.9162 - val_loss: 0.3134 - val_categorical_accuracy: 0.8864 - 567ms/epoch - 28ms/step
Epoch 298/1000
20/20 - 1s - loss: 0.2507 - categorical_accuracy: 0.9101 - val_loss: 0.3217 - val_categorical_accuracy: 0.8830 - 561ms/epoch - 28ms/step
Epoch 299/1000
20/20 - 1s - loss: 0.2478 - categorical_accuracy: 0.9097 - val_loss: 0.2731 - val_categorical_accuracy: 0.9020 - 553ms/epoch - 28ms/step
Epoch 300/1000
20/20 - 1s - loss: 0.2608 - categorical_accuracy: 0.9046 - val_loss: 0.2876 - val_categorical_accuracy: 0.8963 - 564ms/epoch - 28ms/step
Epoch 301/1000
20/20 - 1s - loss: 0.2362 - categorical_accuracy: 0.9162 - val_loss: 0.2727 - val_categorical_accuracy: 0.9036 - 559ms/epoch - 28ms/step
Epoch 302/1000
20/20 - 1s - loss: 0.2591 - categorical_accuracy: 0.9057 - val_loss: 0.3347 - val_categorical_accuracy: 0.8801 - 567ms/epoch - 28ms/step
Epoch 303/1000
20/20 - 1s - loss: 0.2322 - categorical_accuracy: 0.9180 - val_loss: 0.2514 - val_categorical_accuracy: 0.9116 - 558ms/epoch - 28ms/step
Epoch 304/1000
20/20 - 1s - loss: 0.2556 - categorical_accuracy: 0.9062 - val_loss: 0.2532 - val_categorical_accuracy: 0.9111 - 576ms/epoch - 29ms/step
Epoch 305/1000
20/20 - 1s - loss: 0.2511 - categorical_accuracy: 0.9102 - val_loss: 0.3079 - val_categorical_accuracy: 0.8869 - 553ms/epoch - 28ms/step
Epoch 306/1000
20/20 - 1s - loss: 0.3716 - categorical_accuracy: 0.8719 - val_loss: 0.2564 - val_categorical_accuracy: 0.9104 - 565ms/epoch - 28ms/step
Epoch 307/1000
20/20 - 1s - loss: 0.2174 - categorical_accuracy: 0.9245 - val_loss: 0.2772 - val_categorical_accuracy: 0.8993 - 570ms/epoch - 28ms/step
Epoch 308/1000
20/20 - 1s - loss: 0.2504 - categorical_accuracy: 0.9076 - val_loss: 0.2629 - val_categorical_accuracy: 0.9048 - 570ms/epoch - 28ms/step
Epoch 309/1000
20/20 - 1s - loss: 0.2255 - categorical_accuracy: 0.9195 - val_loss: 0.2761 - val_categorical_accuracy: 0.8997 - 559ms/epoch - 28ms/step
Epoch 310/1000
20/20 - 1s - loss: 0.2821 - categorical_accuracy: 0.8957 - val_loss: 0.2561 - val_categorical_accuracy: 0.9094 - 558ms/epoch - 28ms/step
Epoch 311/1000
20/20 - 1s - loss: 0.2294 - categorical_accuracy: 0.9176 - val_loss: 0.2671 - val_categorical_accuracy: 0.9042 - 565ms/epoch - 28ms/step
Epoch 312/1000
20/20 - 1s - loss: 0.2387 - categorical_accuracy: 0.9141 - val_loss: 0.3006 - val_categorical_accuracy: 0.8908 - 589ms/epoch - 29ms/step
Epoch 313/1000
20/20 - 1s - loss: 0.2331 - categorical_accuracy: 0.9172 - val_loss: 0.2492 - val_categorical_accuracy: 0.9126 - 572ms/epoch - 29ms/step
Epoch 314/1000
20/20 - 1s - loss: 0.2535 - categorical_accuracy: 0.9089 - val_loss: 0.3578 - val_categorical_accuracy: 0.8676 - 570ms/epoch - 28ms/step
Epoch 315/1000
20/20 - 1s - loss: 0.2321 - categorical_accuracy: 0.9170 - val_loss: 0.2419 - val_categorical_accuracy: 0.9146 - 560ms/epoch - 28ms/step
Epoch 316/1000
20/20 - 0s - loss: 0.2557 - categorical_accuracy: 0.9078 - val_loss: 0.2952 - val_categorical_accuracy: 0.8935 - 428ms/epoch - 21ms/step
Epoch 317/1000
20/20 - 1s - loss: 0.2393 - categorical_accuracy: 0.9128 - val_loss: 0.2884 - val_categorical_accuracy: 0.8970 - 509ms/epoch - 25ms/step
Epoch 318/1000
20/20 - 1s - loss: 0.2331 - categorical_accuracy: 0.9161 - val_loss: 0.3174 - val_categorical_accuracy: 0.8826 - 561ms/epoch - 28ms/step
Epoch 319/1000
20/20 - 1s - loss: 0.2565 - categorical_accuracy: 0.9073 - val_loss: 0.2594 - val_categorical_accuracy: 0.9065 - 521ms/epoch - 26ms/step
Epoch 320/1000
20/20 - 1s - loss: 0.2316 - categorical_accuracy: 0.9165 - val_loss: 0.2616 - val_categorical_accuracy: 0.9062 - 528ms/epoch - 26ms/step
Epoch 321/1000
20/20 - 1s - loss: 0.2395 - categorical_accuracy: 0.9135 - val_loss: 0.3201 - val_categorical_accuracy: 0.8847 - 568ms/epoch - 28ms/step
Epoch 322/1000
20/20 - 1s - loss: 0.2356 - categorical_accuracy: 0.9153 - val_loss: 0.2510 - val_categorical_accuracy: 0.9112 - 522ms/epoch - 26ms/step
Epoch 323/1000
20/20 - 0s - loss: 0.2274 - categorical_accuracy: 0.9181 - val_loss: 0.2480 - val_categorical_accuracy: 0.9123 - 444ms/epoch - 22ms/step
Epoch 324/1000
20/20 - 0s - loss: 0.4065 - categorical_accuracy: 0.8755 - val_loss: 0.2893 - val_categorical_accuracy: 0.8987 - 468ms/epoch - 23ms/step
Epoch 325/1000
20/20 - 1s - loss: 0.2177 - categorical_accuracy: 0.9252 - val_loss: 0.2453 - val_categorical_accuracy: 0.9140 - 506ms/epoch - 25ms/step
Epoch 326/1000
20/20 - 0s - loss: 0.2427 - categorical_accuracy: 0.9112 - val_loss: 0.2493 - val_categorical_accuracy: 0.9116 - 435ms/epoch - 22ms/step
Epoch 327/1000
20/20 - 0s - loss: 0.2163 - categorical_accuracy: 0.9230 - val_loss: 0.2816 - val_categorical_accuracy: 0.8986 - 422ms/epoch - 21ms/step
Epoch 328/1000
20/20 - 0s - loss: 0.2501 - categorical_accuracy: 0.9094 - val_loss: 0.3462 - val_categorical_accuracy: 0.8754 - 455ms/epoch - 23ms/step
Epoch 329/1000
20/20 - 0s - loss: 0.2275 - categorical_accuracy: 0.9195 - val_loss: 0.2418 - val_categorical_accuracy: 0.9142 - 474ms/epoch - 24ms/step
Epoch 330/1000
20/20 - 0s - loss: 0.2257 - categorical_accuracy: 0.9186 - val_loss: 0.2396 - val_categorical_accuracy: 0.9156 - 463ms/epoch - 23ms/step
Epoch 331/1000
20/20 - 0s - loss: 0.2205 - categorical_accuracy: 0.9211 - val_loss: 0.2539 - val_categorical_accuracy: 0.9108 - 457ms/epoch - 23ms/step
Epoch 332/1000
20/20 - 0s - loss: 0.2612 - categorical_accuracy: 0.9070 - val_loss: 0.2442 - val_categorical_accuracy: 0.9149 - 429ms/epoch - 21ms/step
Epoch 333/1000
20/20 - 1s - loss: 0.2177 - categorical_accuracy: 0.9230 - val_loss: 0.2736 - val_categorical_accuracy: 0.8981 - 522ms/epoch - 26ms/step
Epoch 334/1000
20/20 - 0s - loss: 0.2322 - categorical_accuracy: 0.9149 - val_loss: 0.2456 - val_categorical_accuracy: 0.9127 - 475ms/epoch - 24ms/step
Epoch 335/1000
20/20 - 1s - loss: 0.2160 - categorical_accuracy: 0.9234 - val_loss: 0.3027 - val_categorical_accuracy: 0.8884 - 548ms/epoch - 27ms/step
Epoch 336/1000
20/20 - 1s - loss: 0.2490 - categorical_accuracy: 0.9091 - val_loss: 0.2843 - val_categorical_accuracy: 0.8989 - 631ms/epoch - 32ms/step
Epoch 337/1000
20/20 - 1s - loss: 0.2294 - categorical_accuracy: 0.9166 - val_loss: 0.2710 - val_categorical_accuracy: 0.9019 - 564ms/epoch - 28ms/step
Epoch 338/1000
20/20 - 1s - loss: 0.2254 - categorical_accuracy: 0.9185 - val_loss: 0.2669 - val_categorical_accuracy: 0.9070 - 576ms/epoch - 29ms/step
Epoch 339/1000
20/20 - 1s - loss: 0.2129 - categorical_accuracy: 0.9250 - val_loss: 0.3415 - val_categorical_accuracy: 0.8803 - 567ms/epoch - 28ms/step
Epoch 340/1000
20/20 - 1s - loss: 0.2305 - categorical_accuracy: 0.9175 - val_loss: 0.2455 - val_categorical_accuracy: 0.9140 - 563ms/epoch - 28ms/step
Epoch 341/1000
20/20 - 1s - loss: 0.2093 - categorical_accuracy: 0.9260 - val_loss: 0.2718 - val_categorical_accuracy: 0.9036 - 574ms/epoch - 29ms/step
Epoch 342/1000
20/20 - 1s - loss: 0.2585 - categorical_accuracy: 0.9042 - val_loss: 0.2928 - val_categorical_accuracy: 0.8960 - 565ms/epoch - 28ms/step
Epoch 343/1000
20/20 - 1s - loss: 0.2248 - categorical_accuracy: 0.9211 - val_loss: 0.2389 - val_categorical_accuracy: 0.9154 - 569ms/epoch - 28ms/step
Epoch 344/1000
20/20 - 1s - loss: 0.2067 - categorical_accuracy: 0.9272 - val_loss: 0.2351 - val_categorical_accuracy: 0.9177 - 614ms/epoch - 31ms/step
Epoch 345/1000
20/20 - 1s - loss: 0.2494 - categorical_accuracy: 0.9098 - val_loss: 0.3474 - val_categorical_accuracy: 0.8771 - 572ms/epoch - 29ms/step
Epoch 346/1000
20/20 - 1s - loss: 0.2120 - categorical_accuracy: 0.9256 - val_loss: 0.2491 - val_categorical_accuracy: 0.9122 - 568ms/epoch - 28ms/step
Epoch 347/1000
20/20 - 1s - loss: 0.2344 - categorical_accuracy: 0.9150 - val_loss: 0.2285 - val_categorical_accuracy: 0.9201 - 566ms/epoch - 28ms/step
Epoch 348/1000
20/20 - 1s - loss: 0.1877 - categorical_accuracy: 0.9356 - val_loss: 0.2440 - val_categorical_accuracy: 0.9152 - 555ms/epoch - 28ms/step
Epoch 349/1000
20/20 - 1s - loss: 0.2496 - categorical_accuracy: 0.9091 - val_loss: 0.2367 - val_categorical_accuracy: 0.9170 - 605ms/epoch - 30ms/step
Epoch 350/1000
20/20 - 1s - loss: 0.2284 - categorical_accuracy: 0.9160 - val_loss: 0.3140 - val_categorical_accuracy: 0.8870 - 583ms/epoch - 29ms/step
Epoch 351/1000
20/20 - 1s - loss: 2.4322 - categorical_accuracy: 0.4940 - val_loss: 1.4926 - val_categorical_accuracy: 0.4738 - 574ms/epoch - 29ms/step
Epoch 352/1000
20/20 - 1s - loss: 1.1369 - categorical_accuracy: 0.5977 - val_loss: 0.8461 - val_categorical_accuracy: 0.6925 - 561ms/epoch - 28ms/step
Epoch 353/1000
20/20 - 1s - loss: 0.7000 - categorical_accuracy: 0.7504 - val_loss: 0.5960 - val_categorical_accuracy: 0.7897 - 567ms/epoch - 28ms/step
Epoch 354/1000
20/20 - 1s - loss: 0.5244 - categorical_accuracy: 0.8160 - val_loss: 0.4666 - val_categorical_accuracy: 0.8346 - 567ms/epoch - 28ms/step
Epoch 355/1000
20/20 - 1s - loss: 0.4120 - categorical_accuracy: 0.8551 - val_loss: 0.4147 - val_categorical_accuracy: 0.8534 - 579ms/epoch - 29ms/step
Epoch 356/1000
20/20 - 0s - loss: 0.3651 - categorical_accuracy: 0.8719 - val_loss: 0.4147 - val_categorical_accuracy: 0.8559 - 459ms/epoch - 23ms/step
Epoch 357/1000
20/20 - 1s - loss: 0.3382 - categorical_accuracy: 0.8821 - val_loss: 0.3365 - val_categorical_accuracy: 0.8815 - 503ms/epoch - 25ms/step
Epoch 358/1000
20/20 - 0s - loss: 0.3024 - categorical_accuracy: 0.8925 - val_loss: 0.3057 - val_categorical_accuracy: 0.8932 - 496ms/epoch - 25ms/step
Epoch 359/1000
20/20 - 0s - loss: 0.2795 - categorical_accuracy: 0.9010 - val_loss: 0.3255 - val_categorical_accuracy: 0.8850 - 463ms/epoch - 23ms/step
Epoch 360/1000
20/20 - 0s - loss: 0.2728 - categorical_accuracy: 0.9032 - val_loss: 0.2816 - val_categorical_accuracy: 0.9010 - 498ms/epoch - 25ms/step
Epoch 361/1000
20/20 - 0s - loss: 0.2486 - categorical_accuracy: 0.9133 - val_loss: 0.3429 - val_categorical_accuracy: 0.8782 - 468ms/epoch - 23ms/step
Epoch 362/1000
20/20 - 0s - loss: 0.2882 - categorical_accuracy: 0.8951 - val_loss: 0.2851 - val_categorical_accuracy: 0.9005 - 452ms/epoch - 23ms/step
Epoch 363/1000
20/20 - 1s - loss: 0.2588 - categorical_accuracy: 0.9087 - val_loss: 0.2789 - val_categorical_accuracy: 0.9033 - 508ms/epoch - 25ms/step
Epoch 364/1000
20/20 - 0s - loss: 0.2392 - categorical_accuracy: 0.9154 - val_loss: 0.3006 - val_categorical_accuracy: 0.8894 - 474ms/epoch - 24ms/step
Epoch 365/1000
20/20 - 0s - loss: 0.2453 - categorical_accuracy: 0.9122 - val_loss: 0.2966 - val_categorical_accuracy: 0.8933 - 489ms/epoch - 24ms/step
Epoch 366/1000
20/20 - 1s - loss: 0.2436 - categorical_accuracy: 0.9132 - val_loss: 0.2863 - val_categorical_accuracy: 0.9008 - 569ms/epoch - 28ms/step
Epoch 367/1000
20/20 - 0s - loss: 0.2430 - categorical_accuracy: 0.9128 - val_loss: 0.2757 - val_categorical_accuracy: 0.9031 - 440ms/epoch - 22ms/step
Epoch 368/1000
20/20 - 0s - loss: 0.3203 - categorical_accuracy: 0.8942 - val_loss: 0.2490 - val_categorical_accuracy: 0.9142 - 490ms/epoch - 25ms/step
Epoch 369/1000
20/20 - 0s - loss: 0.2119 - categorical_accuracy: 0.9271 - val_loss: 0.2797 - val_categorical_accuracy: 0.9014 - 412ms/epoch - 21ms/step
Epoch 370/1000
20/20 - 1s - loss: 0.2489 - categorical_accuracy: 0.9110 - val_loss: 0.2527 - val_categorical_accuracy: 0.9125 - 587ms/epoch - 29ms/step
Epoch 371/1000
20/20 - 1s - loss: 0.2216 - categorical_accuracy: 0.9219 - val_loss: 0.3292 - val_categorical_accuracy: 0.8736 - 565ms/epoch - 28ms/step
Epoch 372/1000
20/20 - 0s - loss: 0.2375 - categorical_accuracy: 0.9158 - val_loss: 0.2653 - val_categorical_accuracy: 0.9075 - 472ms/epoch - 24ms/step
Epoch 373/1000
20/20 - 0s - loss: 0.2273 - categorical_accuracy: 0.9195 - val_loss: 0.2867 - val_categorical_accuracy: 0.8977 - 445ms/epoch - 22ms/step
Epoch 374/1000
20/20 - 0s - loss: 0.2310 - categorical_accuracy: 0.9170 - val_loss: 0.3260 - val_categorical_accuracy: 0.8837 - 422ms/epoch - 21ms/step
Epoch 375/1000
20/20 - 0s - loss: 0.2236 - categorical_accuracy: 0.9198 - val_loss: 0.2332 - val_categorical_accuracy: 0.9193 - 432ms/epoch - 22ms/step
Epoch 376/1000
20/20 - 0s - loss: 0.2050 - categorical_accuracy: 0.9282 - val_loss: 0.2968 - val_categorical_accuracy: 0.8949 - 484ms/epoch - 24ms/step
Epoch 377/1000
20/20 - 0s - loss: 0.2354 - categorical_accuracy: 0.9166 - val_loss: 0.2567 - val_categorical_accuracy: 0.9078 - 441ms/epoch - 22ms/step
Epoch 378/1000
20/20 - 0s - loss: 0.2323 - categorical_accuracy: 0.9160 - val_loss: 0.2420 - val_categorical_accuracy: 0.9151 - 469ms/epoch - 23ms/step
Epoch 379/1000
20/20 - 0s - loss: 0.2101 - categorical_accuracy: 0.9263 - val_loss: 0.2382 - val_categorical_accuracy: 0.9168 - 471ms/epoch - 24ms/step
Epoch 380/1000
20/20 - 0s - loss: 0.2129 - categorical_accuracy: 0.9238 - val_loss: 0.2584 - val_categorical_accuracy: 0.9065 - 494ms/epoch - 25ms/step
Epoch 381/1000
20/20 - 1s - loss: 0.2368 - categorical_accuracy: 0.9151 - val_loss: 0.2344 - val_categorical_accuracy: 0.9175 - 577ms/epoch - 29ms/step
Epoch 382/1000
20/20 - 1s - loss: 0.2085 - categorical_accuracy: 0.9263 - val_loss: 0.2401 - val_categorical_accuracy: 0.9137 - 602ms/epoch - 30ms/step
Epoch 383/1000
20/20 - 1s - loss: 0.2056 - categorical_accuracy: 0.9263 - val_loss: 0.2521 - val_categorical_accuracy: 0.9091 - 598ms/epoch - 30ms/step
Epoch 384/1000
20/20 - 1s - loss: 0.2411 - categorical_accuracy: 0.9122 - val_loss: 0.3157 - val_categorical_accuracy: 0.8872 - 574ms/epoch - 29ms/step
Epoch 385/1000
20/20 - 1s - loss: 0.2038 - categorical_accuracy: 0.9294 - val_loss: 0.2908 - val_categorical_accuracy: 0.8984 - 563ms/epoch - 28ms/step
Epoch 386/1000
20/20 - 1s - loss: 0.5439 - categorical_accuracy: 0.8527 - val_loss: 0.2460 - val_categorical_accuracy: 0.9147 - 569ms/epoch - 28ms/step
Epoch 387/1000
20/20 - 1s - loss: 0.1967 - categorical_accuracy: 0.9344 - val_loss: 0.2256 - val_categorical_accuracy: 0.9217 - 580ms/epoch - 29ms/step
Epoch 388/1000
20/20 - 1s - loss: 0.1941 - categorical_accuracy: 0.9325 - val_loss: 0.2546 - val_categorical_accuracy: 0.9095 - 564ms/epoch - 28ms/step
Epoch 389/1000
20/20 - 1s - loss: 0.2115 - categorical_accuracy: 0.9236 - val_loss: 0.2687 - val_categorical_accuracy: 0.9057 - 576ms/epoch - 29ms/step
Epoch 390/1000
20/20 - 1s - loss: 0.2076 - categorical_accuracy: 0.9272 - val_loss: 0.2322 - val_categorical_accuracy: 0.9202 - 554ms/epoch - 28ms/step
Epoch 391/1000
20/20 - 1s - loss: 0.2018 - categorical_accuracy: 0.9291 - val_loss: 0.2448 - val_categorical_accuracy: 0.9122 - 560ms/epoch - 28ms/step
Epoch 392/1000
20/20 - 1s - loss: 0.2102 - categorical_accuracy: 0.9243 - val_loss: 0.2415 - val_categorical_accuracy: 0.9144 - 608ms/epoch - 30ms/step
Epoch 393/1000
20/20 - 1s - loss: 0.2054 - categorical_accuracy: 0.9278 - val_loss: 0.2601 - val_categorical_accuracy: 0.9082 - 521ms/epoch - 26ms/step
Epoch 394/1000
20/20 - 1s - loss: 0.2044 - categorical_accuracy: 0.9274 - val_loss: 0.2471 - val_categorical_accuracy: 0.9126 - 551ms/epoch - 28ms/step
Epoch 395/1000
20/20 - 1s - loss: 0.2158 - categorical_accuracy: 0.9214 - val_loss: 0.2384 - val_categorical_accuracy: 0.9162 - 501ms/epoch - 25ms/step
Epoch 396/1000
20/20 - 1s - loss: 0.1888 - categorical_accuracy: 0.9336 - val_loss: 0.2266 - val_categorical_accuracy: 0.9207 - 535ms/epoch - 27ms/step
Epoch 397/1000
20/20 - 1s - loss: 0.2397 - categorical_accuracy: 0.9132 - val_loss: 0.2553 - val_categorical_accuracy: 0.9100 - 559ms/epoch - 28ms/step
Epoch 398/1000
20/20 - 1s - loss: 0.1799 - categorical_accuracy: 0.9385 - val_loss: 0.2510 - val_categorical_accuracy: 0.9114 - 559ms/epoch - 28ms/step
Epoch 399/1000
20/20 - 1s - loss: 0.2212 - categorical_accuracy: 0.9195 - val_loss: 0.2132 - val_categorical_accuracy: 0.9261 - 575ms/epoch - 29ms/step
Epoch 400/1000
20/20 - 1s - loss: 0.1911 - categorical_accuracy: 0.9344 - val_loss: 0.3777 - val_categorical_accuracy: 0.8739 - 563ms/epoch - 28ms/step
Epoch 401/1000
20/20 - 1s - loss: 0.3183 - categorical_accuracy: 0.9026 - val_loss: 0.2287 - val_categorical_accuracy: 0.9197 - 567ms/epoch - 28ms/step
Epoch 402/1000
20/20 - 1s - loss: 0.1915 - categorical_accuracy: 0.9334 - val_loss: 0.2600 - val_categorical_accuracy: 0.9077 - 564ms/epoch - 28ms/step
Epoch 403/1000
20/20 - 1s - loss: 0.2042 - categorical_accuracy: 0.9268 - val_loss: 0.2281 - val_categorical_accuracy: 0.9199 - 570ms/epoch - 28ms/step
Epoch 404/1000
20/20 - 1s - loss: 0.1848 - categorical_accuracy: 0.9353 - val_loss: 0.2384 - val_categorical_accuracy: 0.9163 - 617ms/epoch - 31ms/step
Epoch 405/1000
20/20 - 1s - loss: 0.1961 - categorical_accuracy: 0.9305 - val_loss: 0.2798 - val_categorical_accuracy: 0.9017 - 587ms/epoch - 29ms/step
Epoch 406/1000
20/20 - 1s - loss: 0.2393 - categorical_accuracy: 0.9138 - val_loss: 0.2169 - val_categorical_accuracy: 0.9250 - 587ms/epoch - 29ms/step
Epoch 407/1000
20/20 - 1s - loss: 0.1882 - categorical_accuracy: 0.9346 - val_loss: 0.2344 - val_categorical_accuracy: 0.9170 - 567ms/epoch - 28ms/step
Epoch 408/1000
20/20 - 1s - loss: 0.1891 - categorical_accuracy: 0.9327 - val_loss: 0.2356 - val_categorical_accuracy: 0.9172 - 544ms/epoch - 27ms/step
Epoch 409/1000
20/20 - 6s - loss: 0.2054 - categorical_accuracy: 0.9268 - val_loss: 0.2544 - val_categorical_accuracy: 0.9115 - 6s/epoch - 321ms/step
Epoch 410/1000
20/20 - 1s - loss: 0.1857 - categorical_accuracy: 0.9358 - val_loss: 0.2155 - val_categorical_accuracy: 0.9251 - 1s/epoch - 57ms/step
Epoch 411/1000
20/20 - 1s - loss: 0.2104 - categorical_accuracy: 0.9247 - val_loss: 0.4046 - val_categorical_accuracy: 0.8533 - 695ms/epoch - 35ms/step
Epoch 412/1000
20/20 - 1s - loss: 0.2048 - categorical_accuracy: 0.9287 - val_loss: 0.2217 - val_categorical_accuracy: 0.9242 - 670ms/epoch - 33ms/step
Epoch 413/1000
20/20 - 2s - loss: 0.1821 - categorical_accuracy: 0.9365 - val_loss: 0.2315 - val_categorical_accuracy: 0.9190 - 2s/epoch - 122ms/step
Epoch 414/1000
20/20 - 3s - loss: 0.2068 - categorical_accuracy: 0.9257 - val_loss: 0.2256 - val_categorical_accuracy: 0.9227 - 3s/epoch - 126ms/step
Epoch 415/1000
20/20 - 1s - loss: 0.1790 - categorical_accuracy: 0.9378 - val_loss: 0.2281 - val_categorical_accuracy: 0.9212 - 938ms/epoch - 47ms/step
Epoch 416/1000
20/20 - 1s - loss: 0.1742 - categorical_accuracy: 0.9399 - val_loss: 0.2185 - val_categorical_accuracy: 0.9242 - 778ms/epoch - 39ms/step
Epoch 417/1000
20/20 - 1s - loss: 0.1966 - categorical_accuracy: 0.9293 - val_loss: 0.3250 - val_categorical_accuracy: 0.8826 - 609ms/epoch - 30ms/step
Epoch 418/1000
20/20 - 1s - loss: 0.3534 - categorical_accuracy: 0.8851 - val_loss: 0.2176 - val_categorical_accuracy: 0.9246 - 644ms/epoch - 32ms/step
Epoch 419/1000
20/20 - 1s - loss: 0.1704 - categorical_accuracy: 0.9425 - val_loss: 0.2227 - val_categorical_accuracy: 0.9215 - 623ms/epoch - 31ms/step
Epoch 420/1000
20/20 - 1s - loss: 0.1823 - categorical_accuracy: 0.9361 - val_loss: 0.2059 - val_categorical_accuracy: 0.9287 - 678ms/epoch - 34ms/step
Epoch 421/1000
20/20 - 1s - loss: 0.1685 - categorical_accuracy: 0.9417 - val_loss: 0.2140 - val_categorical_accuracy: 0.9254 - 666ms/epoch - 33ms/step
Epoch 422/1000
20/20 - 1s - loss: 0.2454 - categorical_accuracy: 0.9165 - val_loss: 0.3722 - val_categorical_accuracy: 0.8668 - 707ms/epoch - 35ms/step
Epoch 423/1000
20/20 - 1s - loss: 0.1772 - categorical_accuracy: 0.9396 - val_loss: 0.2076 - val_categorical_accuracy: 0.9283 - 678ms/epoch - 34ms/step
Epoch 424/1000
20/20 - 1s - loss: 0.1686 - categorical_accuracy: 0.9416 - val_loss: 0.2407 - val_categorical_accuracy: 0.9154 - 678ms/epoch - 34ms/step
Epoch 425/1000
20/20 - 1s - loss: 0.2005 - categorical_accuracy: 0.9274 - val_loss: 0.2131 - val_categorical_accuracy: 0.9264 - 664ms/epoch - 33ms/step
Epoch 426/1000
20/20 - 1s - loss: 0.1749 - categorical_accuracy: 0.9393 - val_loss: 0.2575 - val_categorical_accuracy: 0.9106 - 670ms/epoch - 33ms/step
Epoch 427/1000
20/20 - 1s - loss: 0.2218 - categorical_accuracy: 0.9201 - val_loss: 0.2067 - val_categorical_accuracy: 0.9290 - 612ms/epoch - 31ms/step
Epoch 428/1000
20/20 - 1s - loss: 0.1677 - categorical_accuracy: 0.9423 - val_loss: 0.2354 - val_categorical_accuracy: 0.9156 - 671ms/epoch - 34ms/step
Epoch 429/1000
20/20 - 1s - loss: 0.2062 - categorical_accuracy: 0.9256 - val_loss: 0.2177 - val_categorical_accuracy: 0.9240 - 655ms/epoch - 33ms/step
Epoch 430/1000
20/20 - 1s - loss: 0.1739 - categorical_accuracy: 0.9391 - val_loss: 0.2236 - val_categorical_accuracy: 0.9209 - 576ms/epoch - 29ms/step
Epoch 431/1000
20/20 - 1s - loss: 0.1777 - categorical_accuracy: 0.9373 - val_loss: 0.2052 - val_categorical_accuracy: 0.9299 - 584ms/epoch - 29ms/step
Epoch 432/1000
20/20 - 1s - loss: 0.1750 - categorical_accuracy: 0.9389 - val_loss: 0.2861 - val_categorical_accuracy: 0.8952 - 622ms/epoch - 31ms/step
Epoch 433/1000
20/20 - 1s - loss: 0.2402 - categorical_accuracy: 0.9126 - val_loss: 0.2102 - val_categorical_accuracy: 0.9268 - 670ms/epoch - 33ms/step
Epoch 434/1000
20/20 - 1s - loss: 0.1670 - categorical_accuracy: 0.9420 - val_loss: 0.2385 - val_categorical_accuracy: 0.9150 - 678ms/epoch - 34ms/step
Epoch 435/1000
20/20 - 1s - loss: 0.1791 - categorical_accuracy: 0.9372 - val_loss: 0.2282 - val_categorical_accuracy: 0.9201 - 696ms/epoch - 35ms/step
Epoch 436/1000
20/20 - 1s - loss: 0.1797 - categorical_accuracy: 0.9377 - val_loss: 0.2098 - val_categorical_accuracy: 0.9277 - 723ms/epoch - 36ms/step
Epoch 437/1000
20/20 - 15s - loss: 0.1552 - categorical_accuracy: 0.9472 - val_loss: 0.2100 - val_categorical_accuracy: 0.9274 - 15s/epoch - 746ms/step
Epoch 438/1000
20/20 - 0s - loss: 0.2549 - categorical_accuracy: 0.9115 - val_loss: 0.2127 - val_categorical_accuracy: 0.9260 - 354ms/epoch - 18ms/step
Epoch 439/1000
20/20 - 0s - loss: 0.1851 - categorical_accuracy: 0.9347 - val_loss: 0.2372 - val_categorical_accuracy: 0.9167 - 374ms/epoch - 19ms/step
Epoch 440/1000
20/20 - 0s - loss: 0.1717 - categorical_accuracy: 0.9402 - val_loss: 0.2264 - val_categorical_accuracy: 0.9216 - 368ms/epoch - 18ms/step
Epoch 441/1000
20/20 - 0s - loss: 0.6753 - categorical_accuracy: 0.8420 - val_loss: 1.2162 - val_categorical_accuracy: 0.6456 - 364ms/epoch - 18ms/step
Epoch 442/1000
20/20 - 0s - loss: 0.3173 - categorical_accuracy: 0.8968 - val_loss: 0.2353 - val_categorical_accuracy: 0.9197 - 383ms/epoch - 19ms/step
Epoch 443/1000
20/20 - 0s - loss: 0.1819 - categorical_accuracy: 0.9398 - val_loss: 0.2148 - val_categorical_accuracy: 0.9267 - 366ms/epoch - 18ms/step
Epoch 444/1000
20/20 - 0s - loss: 0.1658 - categorical_accuracy: 0.9451 - val_loss: 0.2053 - val_categorical_accuracy: 0.9300 - 366ms/epoch - 18ms/step
Epoch 445/1000
20/20 - 0s - loss: 0.1698 - categorical_accuracy: 0.9422 - val_loss: 0.2268 - val_categorical_accuracy: 0.9212 - 359ms/epoch - 18ms/step
Epoch 446/1000
20/20 - 0s - loss: 0.1775 - categorical_accuracy: 0.9383 - val_loss: 0.2036 - val_categorical_accuracy: 0.9300 - 360ms/epoch - 18ms/step
Epoch 447/1000
20/20 - 0s - loss: 0.1743 - categorical_accuracy: 0.9395 - val_loss: 0.2211 - val_categorical_accuracy: 0.9232 - 362ms/epoch - 18ms/step
Epoch 448/1000
20/20 - 0s - loss: 0.1793 - categorical_accuracy: 0.9366 - val_loss: 0.3220 - val_categorical_accuracy: 0.8858 - 352ms/epoch - 18ms/step
Epoch 449/1000
20/20 - 0s - loss: 0.1959 - categorical_accuracy: 0.9311 - val_loss: 0.1988 - val_categorical_accuracy: 0.9317 - 336ms/epoch - 17ms/step
Epoch 450/1000
20/20 - 0s - loss: 0.1702 - categorical_accuracy: 0.9407 - val_loss: 0.2971 - val_categorical_accuracy: 0.8981 - 364ms/epoch - 18ms/step
Epoch 451/1000
20/20 - 0s - loss: 0.1687 - categorical_accuracy: 0.9414 - val_loss: 0.2066 - val_categorical_accuracy: 0.9291 - 355ms/epoch - 18ms/step
Epoch 452/1000
20/20 - 0s - loss: 0.1739 - categorical_accuracy: 0.9393 - val_loss: 0.2045 - val_categorical_accuracy: 0.9291 - 348ms/epoch - 17ms/step
Epoch 453/1000
20/20 - 0s - loss: 0.2014 - categorical_accuracy: 0.9280 - val_loss: 0.3559 - val_categorical_accuracy: 0.8743 - 353ms/epoch - 18ms/step
Epoch 454/1000
20/20 - 0s - loss: 0.1703 - categorical_accuracy: 0.9413 - val_loss: 0.2044 - val_categorical_accuracy: 0.9295 - 363ms/epoch - 18ms/step
Epoch 455/1000
20/20 - 0s - loss: 0.1626 - categorical_accuracy: 0.9448 - val_loss: 0.2085 - val_categorical_accuracy: 0.9278 - 383ms/epoch - 19ms/step
Epoch 456/1000
20/20 - 0s - loss: 0.1804 - categorical_accuracy: 0.9356 - val_loss: 0.2286 - val_categorical_accuracy: 0.9206 - 368ms/epoch - 18ms/step
Epoch 457/1000
20/20 - 0s - loss: 0.1734 - categorical_accuracy: 0.9399 - val_loss: 0.2691 - val_categorical_accuracy: 0.9082 - 355ms/epoch - 18ms/step
Epoch 458/1000
20/20 - 0s - loss: 0.1961 - categorical_accuracy: 0.9296 - val_loss: 0.2434 - val_categorical_accuracy: 0.9131 - 372ms/epoch - 19ms/step
Epoch 459/1000
20/20 - 0s - loss: 0.1943 - categorical_accuracy: 0.9314 - val_loss: 0.2038 - val_categorical_accuracy: 0.9302 - 425ms/epoch - 21ms/step
Epoch 460/1000
20/20 - 0s - loss: 0.1528 - categorical_accuracy: 0.9483 - val_loss: 0.2015 - val_categorical_accuracy: 0.9307 - 374ms/epoch - 19ms/step
Epoch 461/1000
20/20 - 0s - loss: 0.1689 - categorical_accuracy: 0.9404 - val_loss: 0.2494 - val_categorical_accuracy: 0.9097 - 356ms/epoch - 18ms/step
Epoch 462/1000
20/20 - 0s - loss: 0.1689 - categorical_accuracy: 0.9409 - val_loss: 0.2564 - val_categorical_accuracy: 0.9108 - 383ms/epoch - 19ms/step
Epoch 463/1000
20/20 - 0s - loss: 0.3620 - categorical_accuracy: 0.8996 - val_loss: 0.2033 - val_categorical_accuracy: 0.9304 - 370ms/epoch - 19ms/step
Epoch 464/1000
20/20 - 0s - loss: 0.1550 - categorical_accuracy: 0.9479 - val_loss: 0.2125 - val_categorical_accuracy: 0.9265 - 356ms/epoch - 18ms/step
Epoch 465/1000
20/20 - 0s - loss: 0.2126 - categorical_accuracy: 0.9241 - val_loss: 0.1966 - val_categorical_accuracy: 0.9334 - 390ms/epoch - 20ms/step
Epoch 466/1000
20/20 - 0s - loss: 0.1450 - categorical_accuracy: 0.9522 - val_loss: 0.1977 - val_categorical_accuracy: 0.9329 - 364ms/epoch - 18ms/step
Epoch 467/1000
20/20 - 0s - loss: 0.1668 - categorical_accuracy: 0.9416 - val_loss: 0.2591 - val_categorical_accuracy: 0.9054 - 358ms/epoch - 18ms/step
Epoch 468/1000
20/20 - 0s - loss: 0.1916 - categorical_accuracy: 0.9313 - val_loss: 0.1954 - val_categorical_accuracy: 0.9326 - 390ms/epoch - 20ms/step
Epoch 469/1000
20/20 - 0s - loss: 0.1544 - categorical_accuracy: 0.9467 - val_loss: 0.2853 - val_categorical_accuracy: 0.9029 - 367ms/epoch - 18ms/step
Epoch 470/1000
20/20 - 0s - loss: 0.1670 - categorical_accuracy: 0.9417 - val_loss: 0.2114 - val_categorical_accuracy: 0.9274 - 371ms/epoch - 19ms/step
Epoch 471/1000
20/20 - 0s - loss: 0.1741 - categorical_accuracy: 0.9389 - val_loss: 0.2425 - val_categorical_accuracy: 0.9162 - 443ms/epoch - 22ms/step
Epoch 472/1000
20/20 - 0s - loss: 0.1579 - categorical_accuracy: 0.9453 - val_loss: 0.2588 - val_categorical_accuracy: 0.9100 - 371ms/epoch - 19ms/step
Epoch 473/1000
20/20 - 0s - loss: 0.2333 - categorical_accuracy: 0.9217 - val_loss: 0.1943 - val_categorical_accuracy: 0.9341 - 374ms/epoch - 19ms/step
Epoch 474/1000
20/20 - 0s - loss: 0.1428 - categorical_accuracy: 0.9525 - val_loss: 0.1960 - val_categorical_accuracy: 0.9332 - 368ms/epoch - 18ms/step
Epoch 475/1000
20/20 - 0s - loss: 0.1592 - categorical_accuracy: 0.9449 - val_loss: 0.2302 - val_categorical_accuracy: 0.9198 - 432ms/epoch - 22ms/step
Epoch 476/1000
20/20 - 0s - loss: 0.1601 - categorical_accuracy: 0.9447 - val_loss: 0.2014 - val_categorical_accuracy: 0.9312 - 364ms/epoch - 18ms/step
Epoch 477/1000
20/20 - 0s - loss: 0.1724 - categorical_accuracy: 0.9391 - val_loss: 0.2008 - val_categorical_accuracy: 0.9312 - 354ms/epoch - 18ms/step
Epoch 478/1000
20/20 - 0s - loss: 0.2220 - categorical_accuracy: 0.9221 - val_loss: 0.3190 - val_categorical_accuracy: 0.8861 - 360ms/epoch - 18ms/step
Epoch 479/1000
20/20 - 0s - loss: 0.1547 - categorical_accuracy: 0.9471 - val_loss: 0.1909 - val_categorical_accuracy: 0.9350 - 356ms/epoch - 18ms/step
Epoch 480/1000
20/20 - 0s - loss: 0.1687 - categorical_accuracy: 0.9409 - val_loss: 0.3209 - val_categorical_accuracy: 0.8836 - 339ms/epoch - 17ms/step
Epoch 481/1000
20/20 - 0s - loss: 0.1712 - categorical_accuracy: 0.9399 - val_loss: 0.2003 - val_categorical_accuracy: 0.9308 - 336ms/epoch - 17ms/step
Epoch 482/1000
20/20 - 0s - loss: 0.1580 - categorical_accuracy: 0.9459 - val_loss: 0.1963 - val_categorical_accuracy: 0.9325 - 368ms/epoch - 18ms/step
Epoch 483/1000
20/20 - 0s - loss: 0.1576 - categorical_accuracy: 0.9452 - val_loss: 0.2059 - val_categorical_accuracy: 0.9294 - 345ms/epoch - 17ms/step
Epoch 484/1000
20/20 - 0s - loss: 0.1848 - categorical_accuracy: 0.9334 - val_loss: 0.2634 - val_categorical_accuracy: 0.9064 - 336ms/epoch - 17ms/step
Epoch 485/1000
20/20 - 0s - loss: 0.1515 - categorical_accuracy: 0.9484 - val_loss: 0.2132 - val_categorical_accuracy: 0.9272 - 357ms/epoch - 18ms/step
Epoch 486/1000
20/20 - 0s - loss: 0.1726 - categorical_accuracy: 0.9413 - val_loss: 0.2636 - val_categorical_accuracy: 0.9061 - 369ms/epoch - 18ms/step
Epoch 487/1000
20/20 - 0s - loss: 0.2295 - categorical_accuracy: 0.9211 - val_loss: 0.1875 - val_categorical_accuracy: 0.9364 - 402ms/epoch - 20ms/step
Epoch 488/1000
20/20 - 0s - loss: 0.1555 - categorical_accuracy: 0.9464 - val_loss: 0.2127 - val_categorical_accuracy: 0.9272 - 412ms/epoch - 21ms/step
Epoch 489/1000
20/20 - 0s - loss: 0.1548 - categorical_accuracy: 0.9458 - val_loss: 0.2497 - val_categorical_accuracy: 0.9148 - 336ms/epoch - 17ms/step
Epoch 490/1000
20/20 - 0s - loss: 0.1531 - categorical_accuracy: 0.9472 - val_loss: 0.1993 - val_categorical_accuracy: 0.9319 - 326ms/epoch - 16ms/step
Epoch 491/1000
20/20 - 0s - loss: 0.1893 - categorical_accuracy: 0.9324 - val_loss: 0.2825 - val_categorical_accuracy: 0.8990 - 334ms/epoch - 17ms/step
Epoch 492/1000
20/20 - 0s - loss: 0.1643 - categorical_accuracy: 0.9424 - val_loss: 0.1921 - val_categorical_accuracy: 0.9347 - 322ms/epoch - 16ms/step
Epoch 493/1000
20/20 - 0s - loss: 0.1543 - categorical_accuracy: 0.9466 - val_loss: 0.1952 - val_categorical_accuracy: 0.9334 - 319ms/epoch - 16ms/step
Epoch 494/1000
20/20 - 0s - loss: 0.1593 - categorical_accuracy: 0.9446 - val_loss: 0.2193 - val_categorical_accuracy: 0.9235 - 350ms/epoch - 18ms/step
Epoch 495/1000
20/20 - 0s - loss: 0.1487 - categorical_accuracy: 0.9494 - val_loss: 0.2194 - val_categorical_accuracy: 0.9236 - 353ms/epoch - 18ms/step
Epoch 496/1000
20/20 - 0s - loss: 0.2050 - categorical_accuracy: 0.9271 - val_loss: 0.1856 - val_categorical_accuracy: 0.9365 - 366ms/epoch - 18ms/step
Epoch 497/1000
20/20 - 0s - loss: 0.1346 - categorical_accuracy: 0.9551 - val_loss: 0.2074 - val_categorical_accuracy: 0.9297 - 357ms/epoch - 18ms/step
Epoch 498/1000
20/20 - 0s - loss: 0.1835 - categorical_accuracy: 0.9357 - val_loss: 0.3827 - val_categorical_accuracy: 0.8709 - 385ms/epoch - 19ms/step
Epoch 499/1000
20/20 - 0s - loss: 0.2103 - categorical_accuracy: 0.9316 - val_loss: 0.1912 - val_categorical_accuracy: 0.9351 - 348ms/epoch - 17ms/step
Epoch 500/1000
20/20 - 0s - loss: 0.1353 - categorical_accuracy: 0.9548 - val_loss: 0.1852 - val_categorical_accuracy: 0.9371 - 399ms/epoch - 20ms/step
Epoch 501/1000
20/20 - 0s - loss: 0.1467 - categorical_accuracy: 0.9500 - val_loss: 0.1944 - val_categorical_accuracy: 0.9344 - 373ms/epoch - 19ms/step
Epoch 502/1000
20/20 - 0s - loss: 0.1620 - categorical_accuracy: 0.9439 - val_loss: 0.2137 - val_categorical_accuracy: 0.9254 - 326ms/epoch - 16ms/step
Epoch 503/1000
20/20 - 0s - loss: 0.2255 - categorical_accuracy: 0.9172 - val_loss: 0.2387 - val_categorical_accuracy: 0.9172 - 341ms/epoch - 17ms/step
Epoch 504/1000
20/20 - 0s - loss: 0.1387 - categorical_accuracy: 0.9539 - val_loss: 0.1899 - val_categorical_accuracy: 0.9356 - 336ms/epoch - 17ms/step
Epoch 505/1000
20/20 - 0s - loss: 0.1524 - categorical_accuracy: 0.9474 - val_loss: 0.3064 - val_categorical_accuracy: 0.8975 - 333ms/epoch - 17ms/step
Epoch 506/1000
20/20 - 0s - loss: 0.1591 - categorical_accuracy: 0.9444 - val_loss: 0.2071 - val_categorical_accuracy: 0.9291 - 328ms/epoch - 16ms/step
Epoch 507/1000
20/20 - 0s - loss: 0.1584 - categorical_accuracy: 0.9448 - val_loss: 0.2189 - val_categorical_accuracy: 0.9254 - 344ms/epoch - 17ms/step
Epoch 508/1000
20/20 - 0s - loss: 0.1490 - categorical_accuracy: 0.9491 - val_loss: 0.2020 - val_categorical_accuracy: 0.9321 - 350ms/epoch - 18ms/step
Epoch 509/1000
20/20 - 0s - loss: 0.1415 - categorical_accuracy: 0.9519 - val_loss: 0.2131 - val_categorical_accuracy: 0.9276 - 366ms/epoch - 18ms/step
Epoch 510/1000
20/20 - 0s - loss: 0.1394 - categorical_accuracy: 0.9529 - val_loss: 0.1947 - val_categorical_accuracy: 0.9341 - 365ms/epoch - 18ms/step
Epoch 511/1000
20/20 - 0s - loss: 0.4313 - categorical_accuracy: 0.8789 - val_loss: 0.2264 - val_categorical_accuracy: 0.9232 - 368ms/epoch - 18ms/step
Epoch 512/1000
20/20 - 0s - loss: 0.1525 - categorical_accuracy: 0.9498 - val_loss: 0.1916 - val_categorical_accuracy: 0.9357 - 353ms/epoch - 18ms/step
Epoch 513/1000
20/20 - 0s - loss: 0.1379 - categorical_accuracy: 0.9542 - val_loss: 0.1988 - val_categorical_accuracy: 0.9322 - 373ms/epoch - 19ms/step
Epoch 514/1000
20/20 - 0s - loss: 0.1891 - categorical_accuracy: 0.9325 - val_loss: 0.3058 - val_categorical_accuracy: 0.8963 - 368ms/epoch - 18ms/step
Epoch 515/1000
20/20 - 0s - loss: 0.1488 - categorical_accuracy: 0.9492 - val_loss: 0.1885 - val_categorical_accuracy: 0.9367 - 351ms/epoch - 18ms/step
Epoch 516/1000
20/20 - 0s - loss: 0.1677 - categorical_accuracy: 0.9404 - val_loss: 0.2180 - val_categorical_accuracy: 0.9238 - 367ms/epoch - 18ms/step
Epoch 517/1000
20/20 - 0s - loss: 0.1379 - categorical_accuracy: 0.9535 - val_loss: 0.1809 - val_categorical_accuracy: 0.9386 - 374ms/epoch - 19ms/step
Epoch 518/1000
20/20 - 0s - loss: 0.1404 - categorical_accuracy: 0.9524 - val_loss: 0.2338 - val_categorical_accuracy: 0.9213 - 370ms/epoch - 19ms/step
Epoch 519/1000
20/20 - 0s - loss: 0.1815 - categorical_accuracy: 0.9346 - val_loss: 0.2102 - val_categorical_accuracy: 0.9267 - 346ms/epoch - 17ms/step
Epoch 520/1000
20/20 - 0s - loss: 0.1615 - categorical_accuracy: 0.9428 - val_loss: 0.1933 - val_categorical_accuracy: 0.9338 - 372ms/epoch - 19ms/step
Epoch 521/1000
20/20 - 0s - loss: 0.1359 - categorical_accuracy: 0.9543 - val_loss: 0.1876 - val_categorical_accuracy: 0.9364 - 364ms/epoch - 18ms/step
Epoch 522/1000
20/20 - 0s - loss: 0.1760 - categorical_accuracy: 0.9362 - val_loss: 0.2866 - val_categorical_accuracy: 0.8974 - 416ms/epoch - 21ms/step
Epoch 523/1000
20/20 - 2s - loss: 0.1519 - categorical_accuracy: 0.9474 - val_loss: 0.1896 - val_categorical_accuracy: 0.9356 - 2s/epoch - 87ms/step
Epoch 524/1000
20/20 - 1s - loss: 0.1361 - categorical_accuracy: 0.9537 - val_loss: 0.2055 - val_categorical_accuracy: 0.9304 - 948ms/epoch - 47ms/step
Epoch 525/1000
20/20 - 1s - loss: 0.1981 - categorical_accuracy: 0.9283 - val_loss: 0.2241 - val_categorical_accuracy: 0.9235 - 1s/epoch - 62ms/step
Epoch 526/1000
20/20 - 1s - loss: 0.1345 - categorical_accuracy: 0.9547 - val_loss: 0.2008 - val_categorical_accuracy: 0.9322 - 650ms/epoch - 32ms/step
Epoch 527/1000
20/20 - 1s - loss: 0.1792 - categorical_accuracy: 0.9368 - val_loss: 0.4384 - val_categorical_accuracy: 0.8570 - 887ms/epoch - 44ms/step
Epoch 528/1000
20/20 - 1s - loss: 0.1574 - categorical_accuracy: 0.9457 - val_loss: 0.1894 - val_categorical_accuracy: 0.9360 - 718ms/epoch - 36ms/step
Epoch 529/1000
20/20 - 1s - loss: 0.1403 - categorical_accuracy: 0.9524 - val_loss: 0.2003 - val_categorical_accuracy: 0.9316 - 531ms/epoch - 27ms/step
Epoch 530/1000
20/20 - 0s - loss: 0.1436 - categorical_accuracy: 0.9505 - val_loss: 0.2052 - val_categorical_accuracy: 0.9307 - 496ms/epoch - 25ms/step
Epoch 531/1000
20/20 - 1s - loss: 0.3120 - categorical_accuracy: 0.9132 - val_loss: 0.1881 - val_categorical_accuracy: 0.9368 - 598ms/epoch - 30ms/step
Epoch 532/1000
20/20 - 1s - loss: 0.1331 - categorical_accuracy: 0.9561 - val_loss: 0.1848 - val_categorical_accuracy: 0.9378 - 609ms/epoch - 30ms/step
Epoch 533/1000
20/20 - 1s - loss: 0.1372 - categorical_accuracy: 0.9531 - val_loss: 0.2153 - val_categorical_accuracy: 0.9246 - 617ms/epoch - 31ms/step
Epoch 534/1000
20/20 - 1s - loss: 0.1585 - categorical_accuracy: 0.9437 - val_loss: 0.1966 - val_categorical_accuracy: 0.9335 - 623ms/epoch - 31ms/step
Epoch 535/1000
20/20 - 1s - loss: 0.2198 - categorical_accuracy: 0.9229 - val_loss: 0.1871 - val_categorical_accuracy: 0.9367 - 616ms/epoch - 31ms/step
Epoch 536/1000
20/20 - 1s - loss: 0.1291 - categorical_accuracy: 0.9572 - val_loss: 0.1862 - val_categorical_accuracy: 0.9368 - 837ms/epoch - 42ms/step
Epoch 537/1000
20/20 - 1s - loss: 0.1610 - categorical_accuracy: 0.9425 - val_loss: 0.2040 - val_categorical_accuracy: 0.9311 - 1s/epoch - 70ms/step
Epoch 538/1000
20/20 - 1s - loss: 0.1328 - categorical_accuracy: 0.9551 - val_loss: 0.2079 - val_categorical_accuracy: 0.9295 - 1s/epoch - 65ms/step
Epoch 539/1000
20/20 - 2s - loss: 0.1391 - categorical_accuracy: 0.9520 - val_loss: 0.1808 - val_categorical_accuracy: 0.9390 - 2s/epoch - 75ms/step
Epoch 540/1000
20/20 - 2s - loss: 0.1276 - categorical_accuracy: 0.9575 - val_loss: 0.2098 - val_categorical_accuracy: 0.9297 - 2s/epoch - 109ms/step
Epoch 541/1000
20/20 - 1s - loss: 0.4174 - categorical_accuracy: 0.8899 - val_loss: 0.4767 - val_categorical_accuracy: 0.8363 - 1s/epoch - 57ms/step
Epoch 542/1000
20/20 - 2s - loss: 0.1814 - categorical_accuracy: 0.9395 - val_loss: 0.1869 - val_categorical_accuracy: 0.9369 - 2s/epoch - 99ms/step
Epoch 543/1000
20/20 - 1s - loss: 0.1318 - categorical_accuracy: 0.9569 - val_loss: 0.1810 - val_categorical_accuracy: 0.9390 - 1s/epoch - 74ms/step
Epoch 544/1000
20/20 - 1s - loss: 0.1266 - categorical_accuracy: 0.9583 - val_loss: 0.1931 - val_categorical_accuracy: 0.9350 - 836ms/epoch - 42ms/step
Epoch 545/1000
20/20 - 1s - loss: 0.1443 - categorical_accuracy: 0.9499 - val_loss: 0.1930 - val_categorical_accuracy: 0.9347 - 1s/epoch - 53ms/step
Epoch 546/1000
20/20 - 1s - loss: 0.1405 - categorical_accuracy: 0.9517 - val_loss: 0.1943 - val_categorical_accuracy: 0.9335 - 1s/epoch - 55ms/step
Epoch 547/1000
20/20 - 1s - loss: 0.1403 - categorical_accuracy: 0.9522 - val_loss: 0.1955 - val_categorical_accuracy: 0.9334 - 713ms/epoch - 36ms/step
Epoch 548/1000
20/20 - 1s - loss: 0.1330 - categorical_accuracy: 0.9551 - val_loss: 0.1777 - val_categorical_accuracy: 0.9401 - 501ms/epoch - 25ms/step
Epoch 549/1000
20/20 - 0s - loss: 0.1350 - categorical_accuracy: 0.9536 - val_loss: 0.3314 - val_categorical_accuracy: 0.8902 - 443ms/epoch - 22ms/step
Epoch 550/1000
20/20 - 1s - loss: 0.2279 - categorical_accuracy: 0.9284 - val_loss: 0.1780 - val_categorical_accuracy: 0.9401 - 765ms/epoch - 38ms/step
Epoch 551/1000
20/20 - 1s - loss: 0.1250 - categorical_accuracy: 0.9589 - val_loss: 0.2027 - val_categorical_accuracy: 0.9296 - 605ms/epoch - 30ms/step
Epoch 552/1000
20/20 - 1s - loss: 0.1611 - categorical_accuracy: 0.9426 - val_loss: 0.1928 - val_categorical_accuracy: 0.9343 - 703ms/epoch - 35ms/step
Epoch 553/1000
20/20 - 1s - loss: 0.1255 - categorical_accuracy: 0.9582 - val_loss: 0.1935 - val_categorical_accuracy: 0.9349 - 750ms/epoch - 37ms/step
Epoch 554/1000
20/20 - 1s - loss: 0.2216 - categorical_accuracy: 0.9244 - val_loss: 0.2829 - val_categorical_accuracy: 0.9053 - 507ms/epoch - 25ms/step
Epoch 555/1000
20/20 - 0s - loss: 0.1381 - categorical_accuracy: 0.9538 - val_loss: 0.1812 - val_categorical_accuracy: 0.9389 - 465ms/epoch - 23ms/step
Epoch 556/1000
20/20 - 0s - loss: 0.1223 - categorical_accuracy: 0.9593 - val_loss: 0.1885 - val_categorical_accuracy: 0.9353 - 443ms/epoch - 22ms/step
Epoch 557/1000
20/20 - 1s - loss: 0.1455 - categorical_accuracy: 0.9497 - val_loss: 0.1904 - val_categorical_accuracy: 0.9349 - 681ms/epoch - 34ms/step
Epoch 558/1000
20/20 - 1s - loss: 0.1343 - categorical_accuracy: 0.9542 - val_loss: 0.2095 - val_categorical_accuracy: 0.9282 - 721ms/epoch - 36ms/step
Epoch 559/1000
20/20 - 1s - loss: 0.1318 - categorical_accuracy: 0.9551 - val_loss: 0.1849 - val_categorical_accuracy: 0.9383 - 689ms/epoch - 34ms/step
Epoch 560/1000
20/20 - 1s - loss: 0.1312 - categorical_accuracy: 0.9557 - val_loss: 0.1872 - val_categorical_accuracy: 0.9366 - 748ms/epoch - 37ms/step
Epoch 561/1000
20/20 - 1s - loss: 0.1312 - categorical_accuracy: 0.9552 - val_loss: 0.1834 - val_categorical_accuracy: 0.9380 - 505ms/epoch - 25ms/step
Epoch 562/1000
20/20 - 0s - loss: 0.4834 - categorical_accuracy: 0.8791 - val_loss: 0.2244 - val_categorical_accuracy: 0.9237 - 484ms/epoch - 24ms/step
Epoch 563/1000
20/20 - 1s - loss: 0.1462 - categorical_accuracy: 0.9525 - val_loss: 0.1828 - val_categorical_accuracy: 0.9393 - 600ms/epoch - 30ms/step
Epoch 564/1000
20/20 - 1s - loss: 0.1294 - categorical_accuracy: 0.9573 - val_loss: 0.1848 - val_categorical_accuracy: 0.9381 - 1s/epoch - 53ms/step
Epoch 565/1000
20/20 - 2s - loss: 0.1244 - categorical_accuracy: 0.9588 - val_loss: 0.1839 - val_categorical_accuracy: 0.9378 - 2s/epoch - 117ms/step
Epoch 566/1000
20/20 - 2s - loss: 0.1369 - categorical_accuracy: 0.9534 - val_loss: 0.2196 - val_categorical_accuracy: 0.9226 - 2s/epoch - 89ms/step
Epoch 567/1000
20/20 - 1s - loss: 0.2063 - categorical_accuracy: 0.9250 - val_loss: 0.2110 - val_categorical_accuracy: 0.9292 - 774ms/epoch - 39ms/step
Epoch 568/1000
20/20 - 1s - loss: 0.1353 - categorical_accuracy: 0.9544 - val_loss: 0.1801 - val_categorical_accuracy: 0.9400 - 628ms/epoch - 31ms/step
Epoch 569/1000
20/20 - 1s - loss: 0.1296 - categorical_accuracy: 0.9560 - val_loss: 0.2133 - val_categorical_accuracy: 0.9295 - 593ms/epoch - 30ms/step
Epoch 570/1000
20/20 - 1s - loss: 0.1336 - categorical_accuracy: 0.9541 - val_loss: 0.1790 - val_categorical_accuracy: 0.9404 - 1s/epoch - 53ms/step
Epoch 571/1000
20/20 - 1s - loss: 0.1455 - categorical_accuracy: 0.9493 - val_loss: 0.2397 - val_categorical_accuracy: 0.9202 - 873ms/epoch - 44ms/step
Epoch 572/1000
20/20 - 1s - loss: 0.1346 - categorical_accuracy: 0.9540 - val_loss: 0.1790 - val_categorical_accuracy: 0.9395 - 791ms/epoch - 40ms/step
Epoch 573/1000
20/20 - 1s - loss: 0.1208 - categorical_accuracy: 0.9597 - val_loss: 0.1831 - val_categorical_accuracy: 0.9397 - 843ms/epoch - 42ms/step
Epoch 574/1000
20/20 - 1s - loss: 0.1687 - categorical_accuracy: 0.9397 - val_loss: 0.2795 - val_categorical_accuracy: 0.9031 - 611ms/epoch - 31ms/step
Epoch 575/1000
20/20 - 1s - loss: 0.1462 - categorical_accuracy: 0.9486 - val_loss: 0.2005 - val_categorical_accuracy: 0.9314 - 623ms/epoch - 31ms/step
Epoch 576/1000
20/20 - 1s - loss: 0.2193 - categorical_accuracy: 0.9240 - val_loss: 0.1776 - val_categorical_accuracy: 0.9398 - 623ms/epoch - 31ms/step
Epoch 577/1000
20/20 - 1s - loss: 0.1174 - categorical_accuracy: 0.9615 - val_loss: 0.1733 - val_categorical_accuracy: 0.9423 - 574ms/epoch - 29ms/step
Epoch 578/1000
20/20 - 1s - loss: 0.1205 - categorical_accuracy: 0.9600 - val_loss: 0.1815 - val_categorical_accuracy: 0.9383 - 652ms/epoch - 33ms/step
Epoch 579/1000
20/20 - 1s - loss: 0.1520 - categorical_accuracy: 0.9462 - val_loss: 0.1774 - val_categorical_accuracy: 0.9406 - 644ms/epoch - 32ms/step
Epoch 580/1000
20/20 - 1s - loss: 0.1168 - categorical_accuracy: 0.9615 - val_loss: 0.1754 - val_categorical_accuracy: 0.9410 - 646ms/epoch - 32ms/step
Epoch 581/1000
20/20 - 1s - loss: 1.0385 - categorical_accuracy: 0.8164 - val_loss: 3.8485 - val_categorical_accuracy: 0.2214 - 799ms/epoch - 40ms/step
Epoch 582/1000
20/20 - 1s - loss: 1.0702 - categorical_accuracy: 0.6617 - val_loss: 0.4646 - val_categorical_accuracy: 0.8401 - 1s/epoch - 63ms/step
Epoch 583/1000
20/20 - 1s - loss: 0.3401 - categorical_accuracy: 0.8838 - val_loss: 0.2984 - val_categorical_accuracy: 0.8986 - 832ms/epoch - 42ms/step
Epoch 584/1000
20/20 - 0s - loss: 0.2262 - categorical_accuracy: 0.9238 - val_loss: 0.2402 - val_categorical_accuracy: 0.9185 - 436ms/epoch - 22ms/step
Epoch 585/1000
20/20 - 1s - loss: 0.1803 - categorical_accuracy: 0.9400 - val_loss: 0.2154 - val_categorical_accuracy: 0.9270 - 860ms/epoch - 43ms/step
Epoch 586/1000
20/20 - 1s - loss: 0.1604 - categorical_accuracy: 0.9463 - val_loss: 0.2004 - val_categorical_accuracy: 0.9324 - 1s/epoch - 54ms/step
Epoch 587/1000
20/20 - 0s - loss: 0.1464 - categorical_accuracy: 0.9512 - val_loss: 0.1971 - val_categorical_accuracy: 0.9341 - 469ms/epoch - 23ms/step
Epoch 588/1000
20/20 - 1s - loss: 0.1453 - categorical_accuracy: 0.9508 - val_loss: 0.2001 - val_categorical_accuracy: 0.9328 - 724ms/epoch - 36ms/step
Epoch 589/1000
20/20 - 1s - loss: 0.1546 - categorical_accuracy: 0.9470 - val_loss: 0.2724 - val_categorical_accuracy: 0.9096 - 716ms/epoch - 36ms/step
Epoch 590/1000
20/20 - 1s - loss: 0.1648 - categorical_accuracy: 0.9427 - val_loss: 0.1848 - val_categorical_accuracy: 0.9387 - 659ms/epoch - 33ms/step
Epoch 591/1000
20/20 - 2s - loss: 0.1370 - categorical_accuracy: 0.9540 - val_loss: 0.2142 - val_categorical_accuracy: 0.9257 - 2s/epoch - 78ms/step
Epoch 592/1000
20/20 - 1s - loss: 0.1666 - categorical_accuracy: 0.9409 - val_loss: 0.1826 - val_categorical_accuracy: 0.9390 - 1s/epoch - 60ms/step
Epoch 593/1000
20/20 - 1s - loss: 0.1262 - categorical_accuracy: 0.9577 - val_loss: 0.1855 - val_categorical_accuracy: 0.9384 - 1s/epoch - 55ms/step
Epoch 594/1000
20/20 - 1s - loss: 0.1323 - categorical_accuracy: 0.9555 - val_loss: 0.2255 - val_categorical_accuracy: 0.9258 - 842ms/epoch - 42ms/step
Epoch 595/1000
20/20 - 2s - loss: 0.1441 - categorical_accuracy: 0.9494 - val_loss: 0.2054 - val_categorical_accuracy: 0.9317 - 2s/epoch - 94ms/step
Epoch 596/1000
20/20 - 2s - loss: 0.2116 - categorical_accuracy: 0.9271 - val_loss: 0.1798 - val_categorical_accuracy: 0.9397 - 2s/epoch - 123ms/step
Epoch 597/1000
20/20 - 2s - loss: 0.1250 - categorical_accuracy: 0.9585 - val_loss: 0.2193 - val_categorical_accuracy: 0.9263 - 2s/epoch - 101ms/step
Epoch 598/1000
20/20 - 12s - loss: 0.1521 - categorical_accuracy: 0.9464 - val_loss: 0.1766 - val_categorical_accuracy: 0.9414 - 12s/epoch - 588ms/step
Epoch 599/1000
20/20 - 3s - loss: 0.1231 - categorical_accuracy: 0.9587 - val_loss: 0.1917 - val_categorical_accuracy: 0.9367 - 3s/epoch - 143ms/step
Epoch 600/1000
20/20 - 1s - loss: 0.1642 - categorical_accuracy: 0.9409 - val_loss: 0.2061 - val_categorical_accuracy: 0.9306 - 1s/epoch - 61ms/step
Epoch 601/1000
20/20 - 1s - loss: 0.1234 - categorical_accuracy: 0.9587 - val_loss: 0.1743 - val_categorical_accuracy: 0.9424 - 805ms/epoch - 40ms/step
Epoch 602/1000
20/20 - 1s - loss: 0.1187 - categorical_accuracy: 0.9606 - val_loss: 0.1993 - val_categorical_accuracy: 0.9333 - 937ms/epoch - 47ms/step
Epoch 603/1000
20/20 - 1s - loss: 0.1436 - categorical_accuracy: 0.9501 - val_loss: 0.1890 - val_categorical_accuracy: 0.9354 - 1s/epoch - 51ms/step
Epoch 604/1000
20/20 - 1s - loss: 0.1373 - categorical_accuracy: 0.9518 - val_loss: 0.1745 - val_categorical_accuracy: 0.9418 - 833ms/epoch - 42ms/step
Epoch 605/1000
20/20 - 1s - loss: 0.1132 - categorical_accuracy: 0.9627 - val_loss: 0.1735 - val_categorical_accuracy: 0.9423 - 1s/epoch - 52ms/step
Epoch 606/1000
20/20 - 1s - loss: 0.1396 - categorical_accuracy: 0.9508 - val_loss: 0.3224 - val_categorical_accuracy: 0.8877 - 1s/epoch - 52ms/step
Epoch 607/1000
20/20 - 1s - loss: 0.1981 - categorical_accuracy: 0.9324 - val_loss: 0.1767 - val_categorical_accuracy: 0.9416 - 696ms/epoch - 35ms/step
Epoch 608/1000
20/20 - 1s - loss: 0.1167 - categorical_accuracy: 0.9613 - val_loss: 0.1763 - val_categorical_accuracy: 0.9415 - 611ms/epoch - 31ms/step
Epoch 609/1000
20/20 - 1s - loss: 0.1248 - categorical_accuracy: 0.9573 - val_loss: 0.2575 - val_categorical_accuracy: 0.9161 - 746ms/epoch - 37ms/step
Epoch 610/1000
20/20 - 1s - loss: 0.2129 - categorical_accuracy: 0.9290 - val_loss: 0.1736 - val_categorical_accuracy: 0.9423 - 923ms/epoch - 46ms/step
Epoch 611/1000
20/20 - 1s - loss: 0.1160 - categorical_accuracy: 0.9619 - val_loss: 0.1788 - val_categorical_accuracy: 0.9407 - 1s/epoch - 65ms/step
Epoch 612/1000
20/20 - 21s - loss: 0.1324 - categorical_accuracy: 0.9543 - val_loss: 0.2098 - val_categorical_accuracy: 0.9299 - 21s/epoch - 1s/step
Epoch 613/1000
20/20 - 2s - loss: 0.1258 - categorical_accuracy: 0.9568 - val_loss: 0.1834 - val_categorical_accuracy: 0.9397 - 2s/epoch - 79ms/step
Epoch 614/1000
20/20 - 1s - loss: 0.1178 - categorical_accuracy: 0.9609 - val_loss: 0.1903 - val_categorical_accuracy: 0.9376 - 1s/epoch - 60ms/step
Epoch 615/1000
20/20 - 1s - loss: 0.1270 - categorical_accuracy: 0.9563 - val_loss: 0.1828 - val_categorical_accuracy: 0.9397 - 1s/epoch - 57ms/step
Epoch 616/1000
20/20 - 1s - loss: 0.1278 - categorical_accuracy: 0.9564 - val_loss: 0.2031 - val_categorical_accuracy: 0.9338 - 1s/epoch - 57ms/step
Epoch 617/1000
20/20 - 1s - loss: 0.2375 - categorical_accuracy: 0.9157 - val_loss: 0.1747 - val_categorical_accuracy: 0.9416 - 949ms/epoch - 47ms/step
Epoch 618/1000
20/20 - 1s - loss: 0.1127 - categorical_accuracy: 0.9631 - val_loss: 0.1720 - val_categorical_accuracy: 0.9429 - 893ms/epoch - 45ms/step
Epoch 619/1000
20/20 - 1s - loss: 0.1145 - categorical_accuracy: 0.9625 - val_loss: 0.1711 - val_categorical_accuracy: 0.9432 - 869ms/epoch - 43ms/step
Epoch 620/1000
20/20 - 1s - loss: 0.1198 - categorical_accuracy: 0.9594 - val_loss: 0.2365 - val_categorical_accuracy: 0.9158 - 969ms/epoch - 48ms/step
Epoch 621/1000
20/20 - 1s - loss: 0.1382 - categorical_accuracy: 0.9517 - val_loss: 0.1785 - val_categorical_accuracy: 0.9409 - 881ms/epoch - 44ms/step
Epoch 622/1000
20/20 - 1s - loss: 0.1220 - categorical_accuracy: 0.9591 - val_loss: 0.2177 - val_categorical_accuracy: 0.9291 - 691ms/epoch - 35ms/step
Epoch 623/1000
20/20 - 0s - loss: 0.1607 - categorical_accuracy: 0.9415 - val_loss: 0.1787 - val_categorical_accuracy: 0.9409 - 325ms/epoch - 16ms/step
Epoch 624/1000
20/20 - 0s - loss: 0.1106 - categorical_accuracy: 0.9638 - val_loss: 0.1738 - val_categorical_accuracy: 0.9429 - 327ms/epoch - 16ms/step
Epoch 625/1000
20/20 - 0s - loss: 0.1213 - categorical_accuracy: 0.9593 - val_loss: 0.2038 - val_categorical_accuracy: 0.9334 - 341ms/epoch - 17ms/step
Epoch 626/1000
20/20 - 0s - loss: 0.1258 - categorical_accuracy: 0.9568 - val_loss: 0.2831 - val_categorical_accuracy: 0.9089 - 355ms/epoch - 18ms/step
Epoch 627/1000
20/20 - 0s - loss: 0.2254 - categorical_accuracy: 0.9321 - val_loss: 0.1709 - val_categorical_accuracy: 0.9433 - 373ms/epoch - 19ms/step
Epoch 628/1000
20/20 - 0s - loss: 0.1090 - categorical_accuracy: 0.9642 - val_loss: 0.1704 - val_categorical_accuracy: 0.9434 - 475ms/epoch - 24ms/step
Epoch 629/1000
20/20 - 0s - loss: 0.1280 - categorical_accuracy: 0.9562 - val_loss: 0.1905 - val_categorical_accuracy: 0.9354 - 380ms/epoch - 19ms/step
Epoch 630/1000
20/20 - 0s - loss: 0.1991 - categorical_accuracy: 0.9305 - val_loss: 0.1694 - val_categorical_accuracy: 0.9438 - 372ms/epoch - 19ms/step
Epoch 631/1000
20/20 - 0s - loss: 0.1091 - categorical_accuracy: 0.9642 - val_loss: 0.1712 - val_categorical_accuracy: 0.9437 - 396ms/epoch - 20ms/step
Epoch 632/1000
20/20 - 0s - loss: 0.1101 - categorical_accuracy: 0.9633 - val_loss: 0.1823 - val_categorical_accuracy: 0.9406 - 378ms/epoch - 19ms/step
Epoch 633/1000
20/20 - 0s - loss: 0.1285 - categorical_accuracy: 0.9559 - val_loss: 0.1832 - val_categorical_accuracy: 0.9396 - 389ms/epoch - 19ms/step
Epoch 634/1000
20/20 - 0s - loss: 0.1079 - categorical_accuracy: 0.9643 - val_loss: 0.1657 - val_categorical_accuracy: 0.9459 - 386ms/epoch - 19ms/step
Epoch 635/1000
20/20 - 0s - loss: 0.1338 - categorical_accuracy: 0.9527 - val_loss: 0.2283 - val_categorical_accuracy: 0.9250 - 390ms/epoch - 20ms/step
Epoch 636/1000
20/20 - 0s - loss: 0.1145 - categorical_accuracy: 0.9617 - val_loss: 0.1711 - val_categorical_accuracy: 0.9444 - 373ms/epoch - 19ms/step
Epoch 637/1000
20/20 - 0s - loss: 0.3887 - categorical_accuracy: 0.9117 - val_loss: 1.0305 - val_categorical_accuracy: 0.7112 - 382ms/epoch - 19ms/step
Epoch 638/1000
20/20 - 0s - loss: 0.2525 - categorical_accuracy: 0.9171 - val_loss: 0.1917 - val_categorical_accuracy: 0.9364 - 367ms/epoch - 18ms/step
Epoch 639/1000
20/20 - 0s - loss: 0.1263 - categorical_accuracy: 0.9590 - val_loss: 0.1777 - val_categorical_accuracy: 0.9409 - 447ms/epoch - 22ms/step
Epoch 640/1000
20/20 - 0s - loss: 0.1139 - categorical_accuracy: 0.9628 - val_loss: 0.1696 - val_categorical_accuracy: 0.9446 - 399ms/epoch - 20ms/step
Epoch 641/1000
20/20 - 1s - loss: 0.1242 - categorical_accuracy: 0.9580 - val_loss: 0.1790 - val_categorical_accuracy: 0.9413 - 513ms/epoch - 26ms/step
Epoch 642/1000
20/20 - 0s - loss: 0.1089 - categorical_accuracy: 0.9645 - val_loss: 0.1694 - val_categorical_accuracy: 0.9445 - 430ms/epoch - 22ms/step
Epoch 643/1000
20/20 - 0s - loss: 0.1128 - categorical_accuracy: 0.9623 - val_loss: 0.2247 - val_categorical_accuracy: 0.9278 - 392ms/epoch - 20ms/step
Epoch 644/1000
20/20 - 0s - loss: 0.2159 - categorical_accuracy: 0.9321 - val_loss: 0.1745 - val_categorical_accuracy: 0.9420 - 390ms/epoch - 20ms/step
Epoch 645/1000
20/20 - 0s - loss: 0.1193 - categorical_accuracy: 0.9597 - val_loss: 0.2215 - val_categorical_accuracy: 0.9227 - 475ms/epoch - 24ms/step
Epoch 646/1000
20/20 - 0s - loss: 0.1480 - categorical_accuracy: 0.9475 - val_loss: 0.1667 - val_categorical_accuracy: 0.9462 - 438ms/epoch - 22ms/step
Epoch 647/1000
20/20 - 0s - loss: 0.1042 - categorical_accuracy: 0.9658 - val_loss: 0.1680 - val_categorical_accuracy: 0.9453 - 477ms/epoch - 24ms/step
Epoch 648/1000
20/20 - 0s - loss: 0.1129 - categorical_accuracy: 0.9621 - val_loss: 0.1994 - val_categorical_accuracy: 0.9348 - 498ms/epoch - 25ms/step
Epoch 649/1000
20/20 - 1s - loss: 0.1248 - categorical_accuracy: 0.9568 - val_loss: 0.1846 - val_categorical_accuracy: 0.9396 - 503ms/epoch - 25ms/step
Epoch 650/1000
20/20 - 1s - loss: 0.1070 - categorical_accuracy: 0.9645 - val_loss: 0.1747 - val_categorical_accuracy: 0.9440 - 531ms/epoch - 27ms/step
Epoch 651/1000
20/20 - 0s - loss: 0.2279 - categorical_accuracy: 0.9251 - val_loss: 0.1980 - val_categorical_accuracy: 0.9321 - 404ms/epoch - 20ms/step
Epoch 652/1000
20/20 - 0s - loss: 0.1134 - categorical_accuracy: 0.9627 - val_loss: 0.1653 - val_categorical_accuracy: 0.9456 - 391ms/epoch - 20ms/step
Epoch 653/1000
20/20 - 0s - loss: 0.1053 - categorical_accuracy: 0.9656 - val_loss: 0.1700 - val_categorical_accuracy: 0.9446 - 437ms/epoch - 22ms/step
Epoch 654/1000
20/20 - 0s - loss: 0.1045 - categorical_accuracy: 0.9653 - val_loss: 0.1827 - val_categorical_accuracy: 0.9411 - 469ms/epoch - 23ms/step
Epoch 655/1000
20/20 - 0s - loss: 0.1988 - categorical_accuracy: 0.9301 - val_loss: 0.1797 - val_categorical_accuracy: 0.9406 - 416ms/epoch - 21ms/step
Epoch 656/1000
20/20 - 0s - loss: 0.1037 - categorical_accuracy: 0.9663 - val_loss: 0.1657 - val_categorical_accuracy: 0.9462 - 374ms/epoch - 19ms/step
Epoch 657/1000
20/20 - 0s - loss: 0.1111 - categorical_accuracy: 0.9630 - val_loss: 0.1707 - val_categorical_accuracy: 0.9438 - 374ms/epoch - 19ms/step
Epoch 658/1000
20/20 - 0s - loss: 0.1279 - categorical_accuracy: 0.9552 - val_loss: 0.1683 - val_categorical_accuracy: 0.9444 - 393ms/epoch - 20ms/step
Epoch 659/1000
20/20 - 0s - loss: 0.1109 - categorical_accuracy: 0.9632 - val_loss: 0.1831 - val_categorical_accuracy: 0.9383 - 380ms/epoch - 19ms/step
Epoch 660/1000
20/20 - 0s - loss: 0.3270 - categorical_accuracy: 0.9177 - val_loss: 0.1822 - val_categorical_accuracy: 0.9397 - 405ms/epoch - 20ms/step
Epoch 661/1000
20/20 - 0s - loss: 0.1120 - categorical_accuracy: 0.9634 - val_loss: 0.1674 - val_categorical_accuracy: 0.9456 - 352ms/epoch - 18ms/step
Epoch 662/1000
20/20 - 0s - loss: 0.1034 - categorical_accuracy: 0.9664 - val_loss: 0.1655 - val_categorical_accuracy: 0.9467 - 367ms/epoch - 18ms/step
Epoch 663/1000
20/20 - 0s - loss: 0.1118 - categorical_accuracy: 0.9625 - val_loss: 0.2710 - val_categorical_accuracy: 0.9120 - 411ms/epoch - 21ms/step
Epoch 664/1000
20/20 - 0s - loss: 0.1702 - categorical_accuracy: 0.9402 - val_loss: 0.1641 - val_categorical_accuracy: 0.9463 - 415ms/epoch - 21ms/step
Epoch 665/1000
20/20 - 1s - loss: 0.1145 - categorical_accuracy: 0.9611 - val_loss: 0.2047 - val_categorical_accuracy: 0.9334 - 501ms/epoch - 25ms/step
Epoch 666/1000
20/20 - 0s - loss: 0.1072 - categorical_accuracy: 0.9642 - val_loss: 0.1674 - val_categorical_accuracy: 0.9460 - 366ms/epoch - 18ms/step
Epoch 667/1000
20/20 - 0s - loss: 0.1098 - categorical_accuracy: 0.9630 - val_loss: 0.1779 - val_categorical_accuracy: 0.9426 - 382ms/epoch - 19ms/step
Epoch 668/1000
20/20 - 0s - loss: 0.1370 - categorical_accuracy: 0.9519 - val_loss: 0.1687 - val_categorical_accuracy: 0.9444 - 394ms/epoch - 20ms/step
Epoch 669/1000
20/20 - 0s - loss: 0.1036 - categorical_accuracy: 0.9660 - val_loss: 0.1934 - val_categorical_accuracy: 0.9377 - 394ms/epoch - 20ms/step
Epoch 670/1000
20/20 - 0s - loss: 0.2224 - categorical_accuracy: 0.9259 - val_loss: 0.1642 - val_categorical_accuracy: 0.9459 - 362ms/epoch - 18ms/step
Epoch 671/1000
20/20 - 0s - loss: 0.1024 - categorical_accuracy: 0.9671 - val_loss: 0.1636 - val_categorical_accuracy: 0.9465 - 376ms/epoch - 19ms/step
Epoch 672/1000
20/20 - 0s - loss: 0.1017 - categorical_accuracy: 0.9669 - val_loss: 0.1751 - val_categorical_accuracy: 0.9414 - 418ms/epoch - 21ms/step
Epoch 673/1000
20/20 - 0s - loss: 0.1131 - categorical_accuracy: 0.9620 - val_loss: 0.1828 - val_categorical_accuracy: 0.9380 - 386ms/epoch - 19ms/step
Epoch 674/1000
20/20 - 0s - loss: 0.1158 - categorical_accuracy: 0.9603 - val_loss: 0.1652 - val_categorical_accuracy: 0.9460 - 378ms/epoch - 19ms/step
Epoch 675/1000
20/20 - 0s - loss: 0.1098 - categorical_accuracy: 0.9635 - val_loss: 0.1779 - val_categorical_accuracy: 0.9401 - 381ms/epoch - 19ms/step
Epoch 676/1000
20/20 - 0s - loss: 0.1302 - categorical_accuracy: 0.9541 - val_loss: 0.1682 - val_categorical_accuracy: 0.9451 - 370ms/epoch - 18ms/step
Epoch 677/1000
20/20 - 0s - loss: 0.1007 - categorical_accuracy: 0.9669 - val_loss: 0.1679 - val_categorical_accuracy: 0.9448 - 379ms/epoch - 19ms/step
Epoch 678/1000
20/20 - 0s - loss: 0.1131 - categorical_accuracy: 0.9609 - val_loss: 0.2142 - val_categorical_accuracy: 0.9275 - 361ms/epoch - 18ms/step
Epoch 679/1000
20/20 - 0s - loss: 0.2203 - categorical_accuracy: 0.9309 - val_loss: 0.1648 - val_categorical_accuracy: 0.9460 - 393ms/epoch - 20ms/step
Epoch 680/1000
20/20 - 0s - loss: 0.1003 - categorical_accuracy: 0.9673 - val_loss: 0.1708 - val_categorical_accuracy: 0.9446 - 371ms/epoch - 19ms/step
Epoch 681/1000
20/20 - 0s - loss: 0.1090 - categorical_accuracy: 0.9638 - val_loss: 0.1750 - val_categorical_accuracy: 0.9438 - 360ms/epoch - 18ms/step
Epoch 682/1000
20/20 - 0s - loss: 0.1981 - categorical_accuracy: 0.9326 - val_loss: 0.1638 - val_categorical_accuracy: 0.9468 - 358ms/epoch - 18ms/step
Epoch 683/1000
20/20 - 0s - loss: 0.0984 - categorical_accuracy: 0.9682 - val_loss: 0.1651 - val_categorical_accuracy: 0.9459 - 392ms/epoch - 20ms/step
Epoch 684/1000
20/20 - 0s - loss: 0.0971 - categorical_accuracy: 0.9683 - val_loss: 0.1628 - val_categorical_accuracy: 0.9473 - 378ms/epoch - 19ms/step
Epoch 685/1000
20/20 - 0s - loss: 0.1065 - categorical_accuracy: 0.9644 - val_loss: 0.2147 - val_categorical_accuracy: 0.9267 - 393ms/epoch - 20ms/step
Epoch 686/1000
20/20 - 0s - loss: 0.3324 - categorical_accuracy: 0.9162 - val_loss: 0.1742 - val_categorical_accuracy: 0.9427 - 387ms/epoch - 19ms/step
Epoch 687/1000
20/20 - 0s - loss: 0.1042 - categorical_accuracy: 0.9663 - val_loss: 0.1635 - val_categorical_accuracy: 0.9475 - 424ms/epoch - 21ms/step
Epoch 688/1000
20/20 - 0s - loss: 0.0996 - categorical_accuracy: 0.9680 - val_loss: 0.1620 - val_categorical_accuracy: 0.9474 - 410ms/epoch - 21ms/step
Epoch 689/1000
20/20 - 0s - loss: 0.1041 - categorical_accuracy: 0.9653 - val_loss: 0.2654 - val_categorical_accuracy: 0.9143 - 380ms/epoch - 19ms/step
Epoch 690/1000
20/20 - 0s - loss: 0.1805 - categorical_accuracy: 0.9348 - val_loss: 0.1646 - val_categorical_accuracy: 0.9465 - 379ms/epoch - 19ms/step
Epoch 691/1000
20/20 - 0s - loss: 0.1032 - categorical_accuracy: 0.9661 - val_loss: 0.2012 - val_categorical_accuracy: 0.9350 - 373ms/epoch - 19ms/step
Epoch 692/1000
20/20 - 0s - loss: 0.1172 - categorical_accuracy: 0.9600 - val_loss: 0.1748 - val_categorical_accuracy: 0.9431 - 364ms/epoch - 18ms/step
Epoch 693/1000
20/20 - 0s - loss: 0.1018 - categorical_accuracy: 0.9668 - val_loss: 0.1668 - val_categorical_accuracy: 0.9470 - 410ms/epoch - 21ms/step
Epoch 694/1000
20/20 - 0s - loss: 0.0970 - categorical_accuracy: 0.9682 - val_loss: 0.1977 - val_categorical_accuracy: 0.9380 - 392ms/epoch - 20ms/step
Epoch 695/1000
20/20 - 0s - loss: 0.2209 - categorical_accuracy: 0.9252 - val_loss: 0.1658 - val_categorical_accuracy: 0.9455 - 380ms/epoch - 19ms/step
Epoch 696/1000
20/20 - 0s - loss: 0.0980 - categorical_accuracy: 0.9685 - val_loss: 0.1624 - val_categorical_accuracy: 0.9478 - 389ms/epoch - 19ms/step
Epoch 697/1000
20/20 - 0s - loss: 0.0972 - categorical_accuracy: 0.9684 - val_loss: 0.1693 - val_categorical_accuracy: 0.9451 - 363ms/epoch - 18ms/step
Epoch 698/1000
20/20 - 0s - loss: 0.1009 - categorical_accuracy: 0.9667 - val_loss: 0.1780 - val_categorical_accuracy: 0.9432 - 413ms/epoch - 21ms/step
Epoch 699/1000
20/20 - 0s - loss: 0.2150 - categorical_accuracy: 0.9291 - val_loss: 0.1885 - val_categorical_accuracy: 0.9373 - 416ms/epoch - 21ms/step
Epoch 700/1000
20/20 - 0s - loss: 0.1007 - categorical_accuracy: 0.9674 - val_loss: 0.1639 - val_categorical_accuracy: 0.9469 - 384ms/epoch - 19ms/step
Epoch 701/1000
20/20 - 0s - loss: 0.1000 - categorical_accuracy: 0.9671 - val_loss: 0.1970 - val_categorical_accuracy: 0.9369 - 406ms/epoch - 20ms/step
Epoch 702/1000
20/20 - 0s - loss: 0.1205 - categorical_accuracy: 0.9583 - val_loss: 0.2415 - val_categorical_accuracy: 0.9224 - 392ms/epoch - 20ms/step
Epoch 703/1000
20/20 - 0s - loss: 0.1596 - categorical_accuracy: 0.9432 - val_loss: 0.1639 - val_categorical_accuracy: 0.9467 - 392ms/epoch - 20ms/step
Epoch 704/1000
20/20 - 0s - loss: 0.0970 - categorical_accuracy: 0.9685 - val_loss: 0.1669 - val_categorical_accuracy: 0.9458 - 395ms/epoch - 20ms/step
Epoch 705/1000
20/20 - 0s - loss: 0.0944 - categorical_accuracy: 0.9695 - val_loss: 0.1666 - val_categorical_accuracy: 0.9460 - 390ms/epoch - 20ms/step
Epoch 706/1000
20/20 - 0s - loss: 0.0960 - categorical_accuracy: 0.9691 - val_loss: 0.1691 - val_categorical_accuracy: 0.9447 - 428ms/epoch - 21ms/step
Epoch 707/1000
20/20 - 0s - loss: 0.1325 - categorical_accuracy: 0.9543 - val_loss: 0.1778 - val_categorical_accuracy: 0.9422 - 374ms/epoch - 19ms/step
Epoch 708/1000
20/20 - 0s - loss: 0.0995 - categorical_accuracy: 0.9670 - val_loss: 0.1653 - val_categorical_accuracy: 0.9472 - 376ms/epoch - 19ms/step
Epoch 709/1000
20/20 - 0s - loss: 0.0942 - categorical_accuracy: 0.9691 - val_loss: 0.1758 - val_categorical_accuracy: 0.9443 - 357ms/epoch - 18ms/step
Epoch 710/1000
20/20 - 0s - loss: 0.2165 - categorical_accuracy: 0.9297 - val_loss: 0.1674 - val_categorical_accuracy: 0.9453 - 352ms/epoch - 18ms/step
Epoch 711/1000
20/20 - 0s - loss: 0.0970 - categorical_accuracy: 0.9689 - val_loss: 0.1633 - val_categorical_accuracy: 0.9479 - 326ms/epoch - 16ms/step
Epoch 712/1000
20/20 - 0s - loss: 0.0947 - categorical_accuracy: 0.9693 - val_loss: 0.1703 - val_categorical_accuracy: 0.9455 - 343ms/epoch - 17ms/step
Epoch 713/1000
20/20 - 0s - loss: 0.1023 - categorical_accuracy: 0.9660 - val_loss: 0.1702 - val_categorical_accuracy: 0.9443 - 336ms/epoch - 17ms/step
Epoch 714/1000
20/20 - 0s - loss: 0.1143 - categorical_accuracy: 0.9605 - val_loss: 0.1917 - val_categorical_accuracy: 0.9356 - 339ms/epoch - 17ms/step
Epoch 715/1000
20/20 - 0s - loss: 0.1157 - categorical_accuracy: 0.9603 - val_loss: 0.1646 - val_categorical_accuracy: 0.9462 - 333ms/epoch - 17ms/step
Epoch 716/1000
20/20 - 0s - loss: 0.1010 - categorical_accuracy: 0.9663 - val_loss: 0.1663 - val_categorical_accuracy: 0.9461 - 350ms/epoch - 17ms/step
Epoch 717/1000
20/20 - 0s - loss: 0.0989 - categorical_accuracy: 0.9674 - val_loss: 0.1645 - val_categorical_accuracy: 0.9473 - 351ms/epoch - 18ms/step
Epoch 718/1000
20/20 - 0s - loss: 0.0932 - categorical_accuracy: 0.9696 - val_loss: 0.1746 - val_categorical_accuracy: 0.9453 - 397ms/epoch - 20ms/step
Epoch 719/1000
20/20 - 0s - loss: 0.1867 - categorical_accuracy: 0.9335 - val_loss: 0.1980 - val_categorical_accuracy: 0.9349 - 356ms/epoch - 18ms/step
Epoch 720/1000
20/20 - 0s - loss: 0.0987 - categorical_accuracy: 0.9678 - val_loss: 0.1666 - val_categorical_accuracy: 0.9463 - 363ms/epoch - 18ms/step
Epoch 721/1000
20/20 - 0s - loss: 0.1004 - categorical_accuracy: 0.9664 - val_loss: 0.1671 - val_categorical_accuracy: 0.9463 - 385ms/epoch - 19ms/step
Epoch 722/1000
20/20 - 0s - loss: 0.0994 - categorical_accuracy: 0.9668 - val_loss: 0.2266 - val_categorical_accuracy: 0.9295 - 364ms/epoch - 18ms/step
Epoch 723/1000
20/20 - 0s - loss: 0.1026 - categorical_accuracy: 0.9657 - val_loss: 0.1617 - val_categorical_accuracy: 0.9479 - 334ms/epoch - 17ms/step
Epoch 724/1000
20/20 - 0s - loss: 0.0888 - categorical_accuracy: 0.9713 - val_loss: 0.1679 - val_categorical_accuracy: 0.9472 - 335ms/epoch - 17ms/step
Epoch 725/1000
20/20 - 0s - loss: 0.4505 - categorical_accuracy: 0.8862 - val_loss: 0.3294 - val_categorical_accuracy: 0.8852 - 338ms/epoch - 17ms/step
Epoch 726/1000
20/20 - 0s - loss: 0.1377 - categorical_accuracy: 0.9538 - val_loss: 0.1676 - val_categorical_accuracy: 0.9451 - 351ms/epoch - 18ms/step
Epoch 727/1000
20/20 - 0s - loss: 0.1002 - categorical_accuracy: 0.9676 - val_loss: 0.1605 - val_categorical_accuracy: 0.9481 - 362ms/epoch - 18ms/step
Epoch 728/1000
20/20 - 0s - loss: 0.0952 - categorical_accuracy: 0.9695 - val_loss: 0.1685 - val_categorical_accuracy: 0.9445 - 366ms/epoch - 18ms/step
Epoch 729/1000
20/20 - 0s - loss: 0.1064 - categorical_accuracy: 0.9645 - val_loss: 0.1712 - val_categorical_accuracy: 0.9437 - 368ms/epoch - 18ms/step
Epoch 730/1000
20/20 - 0s - loss: 0.0961 - categorical_accuracy: 0.9687 - val_loss: 0.1667 - val_categorical_accuracy: 0.9462 - 363ms/epoch - 18ms/step
Epoch 731/1000
20/20 - 0s - loss: 0.0954 - categorical_accuracy: 0.9685 - val_loss: 0.1700 - val_categorical_accuracy: 0.9456 - 372ms/epoch - 19ms/step
Epoch 732/1000
20/20 - 0s - loss: 0.2199 - categorical_accuracy: 0.9315 - val_loss: 0.1942 - val_categorical_accuracy: 0.9335 - 379ms/epoch - 19ms/step
Epoch 733/1000
20/20 - 0s - loss: 0.1021 - categorical_accuracy: 0.9667 - val_loss: 0.1576 - val_categorical_accuracy: 0.9492 - 370ms/epoch - 19ms/step
Epoch 734/1000
20/20 - 0s - loss: 0.0927 - categorical_accuracy: 0.9702 - val_loss: 0.1616 - val_categorical_accuracy: 0.9486 - 383ms/epoch - 19ms/step
Epoch 735/1000
20/20 - 0s - loss: 0.0950 - categorical_accuracy: 0.9691 - val_loss: 0.1721 - val_categorical_accuracy: 0.9447 - 363ms/epoch - 18ms/step
Epoch 736/1000
20/20 - 0s - loss: 0.1076 - categorical_accuracy: 0.9637 - val_loss: 0.2210 - val_categorical_accuracy: 0.9300 - 368ms/epoch - 18ms/step
Epoch 737/1000
20/20 - 0s - loss: 0.1033 - categorical_accuracy: 0.9657 - val_loss: 0.2123 - val_categorical_accuracy: 0.9336 - 386ms/epoch - 19ms/step
Epoch 738/1000
20/20 - 0s - loss: 0.1210 - categorical_accuracy: 0.9575 - val_loss: 0.2986 - val_categorical_accuracy: 0.9077 - 359ms/epoch - 18ms/step
Epoch 739/1000
20/20 - 0s - loss: 0.2046 - categorical_accuracy: 0.9358 - val_loss: 0.1621 - val_categorical_accuracy: 0.9476 - 364ms/epoch - 18ms/step
Epoch 740/1000
20/20 - 0s - loss: 0.0914 - categorical_accuracy: 0.9705 - val_loss: 0.1576 - val_categorical_accuracy: 0.9492 - 365ms/epoch - 18ms/step
Epoch 741/1000
20/20 - 0s - loss: 0.1081 - categorical_accuracy: 0.9630 - val_loss: 0.2365 - val_categorical_accuracy: 0.9175 - 364ms/epoch - 18ms/step
Epoch 742/1000
20/20 - 0s - loss: 0.1677 - categorical_accuracy: 0.9414 - val_loss: 0.1588 - val_categorical_accuracy: 0.9487 - 366ms/epoch - 18ms/step
Epoch 743/1000
20/20 - 0s - loss: 0.0895 - categorical_accuracy: 0.9711 - val_loss: 0.1624 - val_categorical_accuracy: 0.9481 - 366ms/epoch - 18ms/step
Epoch 744/1000
20/20 - 0s - loss: 0.1066 - categorical_accuracy: 0.9638 - val_loss: 0.1733 - val_categorical_accuracy: 0.9455 - 368ms/epoch - 18ms/step
Epoch 745/1000
20/20 - 0s - loss: 0.0889 - categorical_accuracy: 0.9709 - val_loss: 0.1641 - val_categorical_accuracy: 0.9465 - 364ms/epoch - 18ms/step
Epoch 746/1000
20/20 - 0s - loss: 0.0985 - categorical_accuracy: 0.9669 - val_loss: 0.1799 - val_categorical_accuracy: 0.9397 - 357ms/epoch - 18ms/step
Epoch 747/1000
20/20 - 0s - loss: 0.1027 - categorical_accuracy: 0.9653 - val_loss: 0.1630 - val_categorical_accuracy: 0.9469 - 363ms/epoch - 18ms/step
Epoch 748/1000
20/20 - 0s - loss: 0.0887 - categorical_accuracy: 0.9713 - val_loss: 0.1689 - val_categorical_accuracy: 0.9441 - 368ms/epoch - 18ms/step
Epoch 749/1000
20/20 - 0s - loss: 0.1296 - categorical_accuracy: 0.9546 - val_loss: 0.4534 - val_categorical_accuracy: 0.8687 - 368ms/epoch - 18ms/step
Epoch 750/1000
20/20 - 0s - loss: 0.2070 - categorical_accuracy: 0.9337 - val_loss: 0.1622 - val_categorical_accuracy: 0.9479 - 362ms/epoch - 18ms/step
Epoch 751/1000
20/20 - 0s - loss: 0.0924 - categorical_accuracy: 0.9701 - val_loss: 0.1613 - val_categorical_accuracy: 0.9480 - 372ms/epoch - 19ms/step
Epoch 752/1000
20/20 - 0s - loss: 0.0873 - categorical_accuracy: 0.9716 - val_loss: 0.1613 - val_categorical_accuracy: 0.9486 - 356ms/epoch - 18ms/step
Epoch 753/1000
20/20 - 0s - loss: 0.0933 - categorical_accuracy: 0.9695 - val_loss: 0.1884 - val_categorical_accuracy: 0.9407 - 350ms/epoch - 18ms/step
Epoch 754/1000
20/20 - 0s - loss: 0.1272 - categorical_accuracy: 0.9553 - val_loss: 0.2069 - val_categorical_accuracy: 0.9343 - 340ms/epoch - 17ms/step
Epoch 755/1000
20/20 - 0s - loss: 0.0963 - categorical_accuracy: 0.9679 - val_loss: 0.1609 - val_categorical_accuracy: 0.9493 - 379ms/epoch - 19ms/step
Epoch 756/1000
20/20 - 0s - loss: 0.0905 - categorical_accuracy: 0.9709 - val_loss: 0.1792 - val_categorical_accuracy: 0.9440 - 356ms/epoch - 18ms/step
Epoch 757/1000
20/20 - 0s - loss: 0.2061 - categorical_accuracy: 0.9304 - val_loss: 0.1666 - val_categorical_accuracy: 0.9458 - 334ms/epoch - 17ms/step
Epoch 758/1000
20/20 - 0s - loss: 0.0907 - categorical_accuracy: 0.9709 - val_loss: 0.1590 - val_categorical_accuracy: 0.9488 - 333ms/epoch - 17ms/step
Epoch 759/1000
20/20 - 0s - loss: 0.0887 - categorical_accuracy: 0.9712 - val_loss: 0.1563 - val_categorical_accuracy: 0.9496 - 359ms/epoch - 18ms/step
Epoch 760/1000
20/20 - 0s - loss: 0.1106 - categorical_accuracy: 0.9623 - val_loss: 0.2066 - val_categorical_accuracy: 0.9304 - 347ms/epoch - 17ms/step
Epoch 761/1000
20/20 - 0s - loss: 0.1073 - categorical_accuracy: 0.9635 - val_loss: 0.1597 - val_categorical_accuracy: 0.9487 - 356ms/epoch - 18ms/step
Epoch 762/1000
20/20 - 0s - loss: 0.0916 - categorical_accuracy: 0.9698 - val_loss: 0.1576 - val_categorical_accuracy: 0.9500 - 358ms/epoch - 18ms/step
Epoch 763/1000
20/20 - 0s - loss: 0.0901 - categorical_accuracy: 0.9704 - val_loss: 0.1799 - val_categorical_accuracy: 0.9398 - 363ms/epoch - 18ms/step
Epoch 764/1000
20/20 - 0s - loss: 0.1013 - categorical_accuracy: 0.9661 - val_loss: 0.1660 - val_categorical_accuracy: 0.9459 - 371ms/epoch - 19ms/step
Epoch 765/1000
20/20 - 0s - loss: 0.0931 - categorical_accuracy: 0.9695 - val_loss: 0.1627 - val_categorical_accuracy: 0.9488 - 368ms/epoch - 18ms/step
Epoch 766/1000
20/20 - 0s - loss: 0.3342 - categorical_accuracy: 0.9035 - val_loss: 0.1800 - val_categorical_accuracy: 0.9416 - 342ms/epoch - 17ms/step
Epoch 767/1000
20/20 - 0s - loss: 0.1016 - categorical_accuracy: 0.9670 - val_loss: 0.1646 - val_categorical_accuracy: 0.9480 - 349ms/epoch - 17ms/step
Epoch 768/1000
20/20 - 0s - loss: 0.0892 - categorical_accuracy: 0.9713 - val_loss: 0.1569 - val_categorical_accuracy: 0.9511 - 336ms/epoch - 17ms/step
Epoch 769/1000
20/20 - 0s - loss: 0.0921 - categorical_accuracy: 0.9695 - val_loss: 0.1719 - val_categorical_accuracy: 0.9432 - 333ms/epoch - 17ms/step
Epoch 770/1000
20/20 - 0s - loss: 0.1072 - categorical_accuracy: 0.9628 - val_loss: 0.1620 - val_categorical_accuracy: 0.9480 - 384ms/epoch - 19ms/step
Epoch 771/1000
20/20 - 0s - loss: 0.0896 - categorical_accuracy: 0.9708 - val_loss: 0.1630 - val_categorical_accuracy: 0.9472 - 375ms/epoch - 19ms/step
Epoch 772/1000
20/20 - 0s - loss: 0.0926 - categorical_accuracy: 0.9692 - val_loss: 0.1584 - val_categorical_accuracy: 0.9494 - 405ms/epoch - 20ms/step
Epoch 773/1000
20/20 - 0s - loss: 0.1896 - categorical_accuracy: 0.9470 - val_loss: 0.2631 - val_categorical_accuracy: 0.9148 - 376ms/epoch - 19ms/step
Epoch 774/1000
20/20 - 0s - loss: 0.1011 - categorical_accuracy: 0.9664 - val_loss: 0.1564 - val_categorical_accuracy: 0.9501 - 354ms/epoch - 18ms/step
Epoch 775/1000
20/20 - 0s - loss: 0.0973 - categorical_accuracy: 0.9675 - val_loss: 0.2985 - val_categorical_accuracy: 0.9084 - 369ms/epoch - 18ms/step
Epoch 776/1000
20/20 - 0s - loss: 0.1840 - categorical_accuracy: 0.9400 - val_loss: 0.1545 - val_categorical_accuracy: 0.9508 - 363ms/epoch - 18ms/step
Epoch 777/1000
20/20 - 0s - loss: 0.0868 - categorical_accuracy: 0.9723 - val_loss: 0.1601 - val_categorical_accuracy: 0.9487 - 378ms/epoch - 19ms/step
Epoch 778/1000
20/20 - 0s - loss: 0.0877 - categorical_accuracy: 0.9720 - val_loss: 0.1571 - val_categorical_accuracy: 0.9498 - 374ms/epoch - 19ms/step
Epoch 779/1000
20/20 - 0s - loss: 0.1020 - categorical_accuracy: 0.9654 - val_loss: 0.1620 - val_categorical_accuracy: 0.9478 - 401ms/epoch - 20ms/step
Epoch 780/1000
20/20 - 0s - loss: 0.0863 - categorical_accuracy: 0.9721 - val_loss: 0.1686 - val_categorical_accuracy: 0.9450 - 366ms/epoch - 18ms/step
Epoch 781/1000
20/20 - 0s - loss: 0.0908 - categorical_accuracy: 0.9701 - val_loss: 0.1598 - val_categorical_accuracy: 0.9493 - 366ms/epoch - 18ms/step
Epoch 782/1000
20/20 - 0s - loss: 0.0846 - categorical_accuracy: 0.9725 - val_loss: 0.1631 - val_categorical_accuracy: 0.9487 - 349ms/epoch - 17ms/step
Epoch 783/1000
20/20 - 0s - loss: 0.1156 - categorical_accuracy: 0.9621 - val_loss: 0.1649 - val_categorical_accuracy: 0.9493 - 364ms/epoch - 18ms/step
Epoch 784/1000
20/20 - 0s - loss: 0.2112 - categorical_accuracy: 0.9291 - val_loss: 0.2308 - val_categorical_accuracy: 0.9203 - 354ms/epoch - 18ms/step
Epoch 785/1000
20/20 - 0s - loss: 0.0989 - categorical_accuracy: 0.9674 - val_loss: 0.1544 - val_categorical_accuracy: 0.9511 - 376ms/epoch - 19ms/step
Epoch 786/1000
20/20 - 0s - loss: 0.0852 - categorical_accuracy: 0.9724 - val_loss: 0.1710 - val_categorical_accuracy: 0.9465 - 389ms/epoch - 19ms/step
Epoch 787/1000
20/20 - 0s - loss: 0.0941 - categorical_accuracy: 0.9691 - val_loss: 0.1873 - val_categorical_accuracy: 0.9420 - 396ms/epoch - 20ms/step
Epoch 788/1000
20/20 - 0s - loss: 0.3165 - categorical_accuracy: 0.9108 - val_loss: 0.1884 - val_categorical_accuracy: 0.9378 - 386ms/epoch - 19ms/step
Epoch 789/1000
20/20 - 0s - loss: 0.0980 - categorical_accuracy: 0.9682 - val_loss: 0.1564 - val_categorical_accuracy: 0.9493 - 377ms/epoch - 19ms/step
Epoch 790/1000
20/20 - 0s - loss: 0.0868 - categorical_accuracy: 0.9722 - val_loss: 0.1564 - val_categorical_accuracy: 0.9502 - 400ms/epoch - 20ms/step
Epoch 791/1000
20/20 - 0s - loss: 0.0846 - categorical_accuracy: 0.9728 - val_loss: 0.1645 - val_categorical_accuracy: 0.9488 - 356ms/epoch - 18ms/step
Epoch 792/1000
20/20 - 0s - loss: 0.0896 - categorical_accuracy: 0.9706 - val_loss: 0.1577 - val_categorical_accuracy: 0.9502 - 405ms/epoch - 20ms/step
Epoch 793/1000
20/20 - 0s - loss: 0.0871 - categorical_accuracy: 0.9716 - val_loss: 0.1678 - val_categorical_accuracy: 0.9476 - 353ms/epoch - 18ms/step
Epoch 794/1000
20/20 - 0s - loss: 0.0953 - categorical_accuracy: 0.9677 - val_loss: 0.1819 - val_categorical_accuracy: 0.9431 - 353ms/epoch - 18ms/step
Epoch 795/1000
20/20 - 0s - loss: 0.1083 - categorical_accuracy: 0.9621 - val_loss: 0.1685 - val_categorical_accuracy: 0.9478 - 356ms/epoch - 18ms/step
Epoch 796/1000
20/20 - 0s - loss: 0.0847 - categorical_accuracy: 0.9723 - val_loss: 0.1572 - val_categorical_accuracy: 0.9508 - 361ms/epoch - 18ms/step
Epoch 797/1000
20/20 - 0s - loss: 0.0884 - categorical_accuracy: 0.9708 - val_loss: 0.1696 - val_categorical_accuracy: 0.9440 - 353ms/epoch - 18ms/step
Epoch 798/1000
20/20 - 0s - loss: 0.1169 - categorical_accuracy: 0.9584 - val_loss: 0.1581 - val_categorical_accuracy: 0.9493 - 358ms/epoch - 18ms/step
Epoch 799/1000
20/20 - 0s - loss: 0.0825 - categorical_accuracy: 0.9732 - val_loss: 0.1586 - val_categorical_accuracy: 0.9506 - 348ms/epoch - 17ms/step
Epoch 800/1000
20/20 - 0s - loss: 0.0898 - categorical_accuracy: 0.9702 - val_loss: 0.1739 - val_categorical_accuracy: 0.9426 - 358ms/epoch - 18ms/step
Epoch 801/1000
20/20 - 0s - loss: 0.0923 - categorical_accuracy: 0.9693 - val_loss: 0.1624 - val_categorical_accuracy: 0.9474 - 358ms/epoch - 18ms/step
Epoch 802/1000
20/20 - 0s - loss: 0.2233 - categorical_accuracy: 0.9292 - val_loss: 0.2332 - val_categorical_accuracy: 0.9239 - 344ms/epoch - 17ms/step
Epoch 803/1000
20/20 - 0s - loss: 0.0949 - categorical_accuracy: 0.9691 - val_loss: 0.1587 - val_categorical_accuracy: 0.9496 - 358ms/epoch - 18ms/step
Epoch 804/1000
20/20 - 0s - loss: 0.0868 - categorical_accuracy: 0.9717 - val_loss: 0.1544 - val_categorical_accuracy: 0.9514 - 361ms/epoch - 18ms/step
Epoch 805/1000
20/20 - 0s - loss: 0.0825 - categorical_accuracy: 0.9733 - val_loss: 0.1555 - val_categorical_accuracy: 0.9511 - 354ms/epoch - 18ms/step
Epoch 806/1000
20/20 - 0s - loss: 0.0854 - categorical_accuracy: 0.9724 - val_loss: 0.1593 - val_categorical_accuracy: 0.9499 - 353ms/epoch - 18ms/step
Epoch 807/1000
20/20 - 0s - loss: 0.0919 - categorical_accuracy: 0.9693 - val_loss: 0.2009 - val_categorical_accuracy: 0.9309 - 377ms/epoch - 19ms/step
Epoch 808/1000
20/20 - 0s - loss: 0.3630 - categorical_accuracy: 0.9016 - val_loss: 0.1753 - val_categorical_accuracy: 0.9429 - 369ms/epoch - 18ms/step
Epoch 809/1000
20/20 - 0s - loss: 0.0968 - categorical_accuracy: 0.9692 - val_loss: 0.1580 - val_categorical_accuracy: 0.9494 - 365ms/epoch - 18ms/step
Epoch 810/1000
20/20 - 0s - loss: 0.0856 - categorical_accuracy: 0.9730 - val_loss: 0.1555 - val_categorical_accuracy: 0.9512 - 382ms/epoch - 19ms/step
Epoch 811/1000
20/20 - 0s - loss: 0.0820 - categorical_accuracy: 0.9740 - val_loss: 0.1555 - val_categorical_accuracy: 0.9514 - 367ms/epoch - 18ms/step
Epoch 812/1000
20/20 - 0s - loss: 0.0821 - categorical_accuracy: 0.9737 - val_loss: 0.1604 - val_categorical_accuracy: 0.9493 - 383ms/epoch - 19ms/step
Epoch 813/1000
20/20 - 0s - loss: 0.0865 - categorical_accuracy: 0.9719 - val_loss: 0.1746 - val_categorical_accuracy: 0.9462 - 425ms/epoch - 21ms/step
Epoch 814/1000
20/20 - 0s - loss: 0.1029 - categorical_accuracy: 0.9643 - val_loss: 0.1883 - val_categorical_accuracy: 0.9418 - 392ms/epoch - 20ms/step
Epoch 815/1000
20/20 - 0s - loss: 0.0904 - categorical_accuracy: 0.9696 - val_loss: 0.1690 - val_categorical_accuracy: 0.9475 - 360ms/epoch - 18ms/step
Epoch 816/1000
20/20 - 0s - loss: 0.0810 - categorical_accuracy: 0.9736 - val_loss: 0.1603 - val_categorical_accuracy: 0.9491 - 347ms/epoch - 17ms/step
Epoch 817/1000
20/20 - 0s - loss: 0.0865 - categorical_accuracy: 0.9717 - val_loss: 0.2167 - val_categorical_accuracy: 0.9280 - 354ms/epoch - 18ms/step
Epoch 818/1000
20/20 - 0s - loss: 0.1186 - categorical_accuracy: 0.9590 - val_loss: 0.1695 - val_categorical_accuracy: 0.9440 - 353ms/epoch - 18ms/step
Epoch 819/1000
20/20 - 0s - loss: 0.1910 - categorical_accuracy: 0.9391 - val_loss: 0.1588 - val_categorical_accuracy: 0.9491 - 352ms/epoch - 18ms/step
Epoch 820/1000
20/20 - 0s - loss: 0.0826 - categorical_accuracy: 0.9738 - val_loss: 0.1533 - val_categorical_accuracy: 0.9518 - 352ms/epoch - 18ms/step
Epoch 821/1000
20/20 - 0s - loss: 0.0796 - categorical_accuracy: 0.9747 - val_loss: 0.1601 - val_categorical_accuracy: 0.9504 - 403ms/epoch - 20ms/step
Epoch 822/1000
20/20 - 0s - loss: 0.0847 - categorical_accuracy: 0.9726 - val_loss: 0.1599 - val_categorical_accuracy: 0.9507 - 351ms/epoch - 18ms/step
Epoch 823/1000
20/20 - 0s - loss: 0.0812 - categorical_accuracy: 0.9735 - val_loss: 0.1724 - val_categorical_accuracy: 0.9440 - 402ms/epoch - 20ms/step
Epoch 824/1000
20/20 - 0s - loss: 0.1961 - categorical_accuracy: 0.9337 - val_loss: 0.1634 - val_categorical_accuracy: 0.9483 - 330ms/epoch - 17ms/step
Epoch 825/1000
20/20 - 0s - loss: 0.0842 - categorical_accuracy: 0.9729 - val_loss: 0.1594 - val_categorical_accuracy: 0.9502 - 328ms/epoch - 16ms/step
Epoch 826/1000
20/20 - 0s - loss: 0.0829 - categorical_accuracy: 0.9729 - val_loss: 0.1600 - val_categorical_accuracy: 0.9502 - 329ms/epoch - 16ms/step
Epoch 827/1000
20/20 - 0s - loss: 0.0836 - categorical_accuracy: 0.9727 - val_loss: 0.1753 - val_categorical_accuracy: 0.9467 - 330ms/epoch - 17ms/step
Epoch 828/1000
20/20 - 0s - loss: 0.2305 - categorical_accuracy: 0.9328 - val_loss: 0.1688 - val_categorical_accuracy: 0.9454 - 330ms/epoch - 17ms/step
Epoch 829/1000
20/20 - 0s - loss: 0.0862 - categorical_accuracy: 0.9723 - val_loss: 0.1554 - val_categorical_accuracy: 0.9513 - 343ms/epoch - 17ms/step
Epoch 830/1000
20/20 - 0s - loss: 0.0813 - categorical_accuracy: 0.9740 - val_loss: 0.1564 - val_categorical_accuracy: 0.9503 - 351ms/epoch - 18ms/step
Epoch 831/1000
20/20 - 0s - loss: 0.0832 - categorical_accuracy: 0.9731 - val_loss: 0.1576 - val_categorical_accuracy: 0.9496 - 351ms/epoch - 18ms/step
Epoch 832/1000
20/20 - 0s - loss: 0.0943 - categorical_accuracy: 0.9684 - val_loss: 0.1664 - val_categorical_accuracy: 0.9458 - 401ms/epoch - 20ms/step
Epoch 833/1000
20/20 - 0s - loss: 0.0911 - categorical_accuracy: 0.9704 - val_loss: 0.2453 - val_categorical_accuracy: 0.9248 - 389ms/epoch - 19ms/step
Epoch 834/1000
20/20 - 0s - loss: 0.0947 - categorical_accuracy: 0.9688 - val_loss: 0.1561 - val_categorical_accuracy: 0.9516 - 352ms/epoch - 18ms/step
Epoch 835/1000
20/20 - 0s - loss: 0.0801 - categorical_accuracy: 0.9741 - val_loss: 0.1544 - val_categorical_accuracy: 0.9525 - 373ms/epoch - 19ms/step
Epoch 836/1000
20/20 - 0s - loss: 0.0791 - categorical_accuracy: 0.9741 - val_loss: 0.1794 - val_categorical_accuracy: 0.9447 - 370ms/epoch - 19ms/step
Epoch 837/1000
20/20 - 0s - loss: 0.3187 - categorical_accuracy: 0.9024 - val_loss: 0.4789 - val_categorical_accuracy: 0.8638 - 345ms/epoch - 17ms/step
Epoch 838/1000
20/20 - 0s - loss: 0.1212 - categorical_accuracy: 0.9617 - val_loss: 0.1570 - val_categorical_accuracy: 0.9497 - 377ms/epoch - 19ms/step
Epoch 839/1000
20/20 - 0s - loss: 0.0828 - categorical_accuracy: 0.9738 - val_loss: 0.1549 - val_categorical_accuracy: 0.9513 - 362ms/epoch - 18ms/step
Epoch 840/1000
20/20 - 0s - loss: 0.0822 - categorical_accuracy: 0.9736 - val_loss: 0.1673 - val_categorical_accuracy: 0.9454 - 346ms/epoch - 17ms/step
Epoch 841/1000
20/20 - 0s - loss: 0.0943 - categorical_accuracy: 0.9681 - val_loss: 0.1548 - val_categorical_accuracy: 0.9511 - 350ms/epoch - 18ms/step
Epoch 842/1000
20/20 - 0s - loss: 0.0785 - categorical_accuracy: 0.9747 - val_loss: 0.1562 - val_categorical_accuracy: 0.9520 - 380ms/epoch - 19ms/step
Epoch 843/1000
20/20 - 0s - loss: 0.0841 - categorical_accuracy: 0.9725 - val_loss: 0.1611 - val_categorical_accuracy: 0.9476 - 365ms/epoch - 18ms/step
Epoch 844/1000
20/20 - 0s - loss: 0.0950 - categorical_accuracy: 0.9674 - val_loss: 0.1797 - val_categorical_accuracy: 0.9405 - 362ms/epoch - 18ms/step
Epoch 845/1000
20/20 - 0s - loss: 0.0915 - categorical_accuracy: 0.9690 - val_loss: 0.1557 - val_categorical_accuracy: 0.9519 - 371ms/epoch - 19ms/step
Epoch 846/1000
20/20 - 0s - loss: 0.0809 - categorical_accuracy: 0.9739 - val_loss: 0.1584 - val_categorical_accuracy: 0.9513 - 347ms/epoch - 17ms/step
Epoch 847/1000
20/20 - 0s - loss: 0.1028 - categorical_accuracy: 0.9642 - val_loss: 0.2019 - val_categorical_accuracy: 0.9383 - 365ms/epoch - 18ms/step
Epoch 848/1000
20/20 - 0s - loss: 0.0904 - categorical_accuracy: 0.9694 - val_loss: 0.2446 - val_categorical_accuracy: 0.9183 - 349ms/epoch - 17ms/step
Epoch 849/1000
20/20 - 0s - loss: 0.1914 - categorical_accuracy: 0.9407 - val_loss: 0.1543 - val_categorical_accuracy: 0.9515 - 351ms/epoch - 18ms/step
Epoch 850/1000
20/20 - 0s - loss: 0.0792 - categorical_accuracy: 0.9746 - val_loss: 0.1519 - val_categorical_accuracy: 0.9524 - 364ms/epoch - 18ms/step
Epoch 851/1000
20/20 - 0s - loss: 0.0782 - categorical_accuracy: 0.9749 - val_loss: 0.1546 - val_categorical_accuracy: 0.9524 - 358ms/epoch - 18ms/step
Epoch 852/1000
20/20 - 0s - loss: 0.0857 - categorical_accuracy: 0.9717 - val_loss: 0.1937 - val_categorical_accuracy: 0.9412 - 354ms/epoch - 18ms/step
Epoch 853/1000
20/20 - 0s - loss: 0.1402 - categorical_accuracy: 0.9495 - val_loss: 0.5212 - val_categorical_accuracy: 0.8589 - 356ms/epoch - 18ms/step
Epoch 854/1000
20/20 - 0s - loss: 0.1506 - categorical_accuracy: 0.9526 - val_loss: 0.1562 - val_categorical_accuracy: 0.9511 - 336ms/epoch - 17ms/step
Epoch 855/1000
20/20 - 0s - loss: 0.0784 - categorical_accuracy: 0.9752 - val_loss: 0.1605 - val_categorical_accuracy: 0.9505 - 357ms/epoch - 18ms/step
Epoch 856/1000
20/20 - 0s - loss: 0.0824 - categorical_accuracy: 0.9726 - val_loss: 0.1699 - val_categorical_accuracy: 0.9483 - 353ms/epoch - 18ms/step
Epoch 857/1000
20/20 - 0s - loss: 0.0784 - categorical_accuracy: 0.9747 - val_loss: 0.1586 - val_categorical_accuracy: 0.9501 - 348ms/epoch - 17ms/step
Epoch 858/1000
20/20 - 0s - loss: 0.0788 - categorical_accuracy: 0.9746 - val_loss: 0.1699 - val_categorical_accuracy: 0.9473 - 364ms/epoch - 18ms/step
Epoch 859/1000
20/20 - 0s - loss: 0.0846 - categorical_accuracy: 0.9723 - val_loss: 0.1724 - val_categorical_accuracy: 0.9443 - 364ms/epoch - 18ms/step
Epoch 860/1000
20/20 - 0s - loss: 0.2257 - categorical_accuracy: 0.9346 - val_loss: 0.1550 - val_categorical_accuracy: 0.9506 - 352ms/epoch - 18ms/step
Epoch 861/1000
20/20 - 0s - loss: 0.0804 - categorical_accuracy: 0.9746 - val_loss: 0.1543 - val_categorical_accuracy: 0.9515 - 367ms/epoch - 18ms/step
Epoch 862/1000
20/20 - 0s - loss: 0.0815 - categorical_accuracy: 0.9734 - val_loss: 0.1527 - val_categorical_accuracy: 0.9529 - 351ms/epoch - 18ms/step
Epoch 863/1000
20/20 - 0s - loss: 0.0769 - categorical_accuracy: 0.9755 - val_loss: 0.1546 - val_categorical_accuracy: 0.9521 - 354ms/epoch - 18ms/step
Epoch 864/1000
20/20 - 0s - loss: 0.0797 - categorical_accuracy: 0.9740 - val_loss: 0.1533 - val_categorical_accuracy: 0.9518 - 368ms/epoch - 18ms/step
Epoch 865/1000
20/20 - 0s - loss: 0.0847 - categorical_accuracy: 0.9719 - val_loss: 0.1749 - val_categorical_accuracy: 0.9433 - 394ms/epoch - 20ms/step
Epoch 866/1000
20/20 - 0s - loss: 0.2421 - categorical_accuracy: 0.9292 - val_loss: 0.1786 - val_categorical_accuracy: 0.9426 - 360ms/epoch - 18ms/step
Epoch 867/1000
20/20 - 0s - loss: 0.0868 - categorical_accuracy: 0.9719 - val_loss: 0.1544 - val_categorical_accuracy: 0.9512 - 370ms/epoch - 19ms/step
Epoch 868/1000
20/20 - 0s - loss: 0.0771 - categorical_accuracy: 0.9758 - val_loss: 0.1543 - val_categorical_accuracy: 0.9526 - 362ms/epoch - 18ms/step
Epoch 869/1000
20/20 - 0s - loss: 0.0798 - categorical_accuracy: 0.9745 - val_loss: 0.1595 - val_categorical_accuracy: 0.9518 - 381ms/epoch - 19ms/step
Epoch 870/1000
20/20 - 0s - loss: 0.2113 - categorical_accuracy: 0.9331 - val_loss: 0.2140 - val_categorical_accuracy: 0.9310 - 368ms/epoch - 18ms/step
Epoch 871/1000
20/20 - 0s - loss: 0.0910 - categorical_accuracy: 0.9708 - val_loss: 0.1574 - val_categorical_accuracy: 0.9503 - 344ms/epoch - 17ms/step
Epoch 872/1000
20/20 - 0s - loss: 0.0785 - categorical_accuracy: 0.9749 - val_loss: 0.1517 - val_categorical_accuracy: 0.9535 - 368ms/epoch - 18ms/step
Epoch 873/1000
20/20 - 0s - loss: 0.0777 - categorical_accuracy: 0.9752 - val_loss: 0.1553 - val_categorical_accuracy: 0.9513 - 354ms/epoch - 18ms/step
Epoch 874/1000
20/20 - 0s - loss: 0.0780 - categorical_accuracy: 0.9749 - val_loss: 0.1659 - val_categorical_accuracy: 0.9464 - 354ms/epoch - 18ms/step
Epoch 875/1000
20/20 - 0s - loss: 0.0945 - categorical_accuracy: 0.9679 - val_loss: 0.1515 - val_categorical_accuracy: 0.9526 - 371ms/epoch - 19ms/step
Epoch 876/1000
20/20 - 0s - loss: 0.0736 - categorical_accuracy: 0.9766 - val_loss: 0.1558 - val_categorical_accuracy: 0.9525 - 348ms/epoch - 17ms/step
Epoch 877/1000
20/20 - 0s - loss: 0.0784 - categorical_accuracy: 0.9748 - val_loss: 0.1719 - val_categorical_accuracy: 0.9430 - 364ms/epoch - 18ms/step
Epoch 878/1000
20/20 - 0s - loss: 0.2099 - categorical_accuracy: 0.9319 - val_loss: 0.1615 - val_categorical_accuracy: 0.9484 - 364ms/epoch - 18ms/step
Epoch 879/1000
20/20 - 0s - loss: 0.0796 - categorical_accuracy: 0.9745 - val_loss: 0.1511 - val_categorical_accuracy: 0.9528 - 340ms/epoch - 17ms/step
Epoch 880/1000
20/20 - 0s - loss: 0.0756 - categorical_accuracy: 0.9758 - val_loss: 0.1504 - val_categorical_accuracy: 0.9529 - 370ms/epoch - 19ms/step
Epoch 881/1000
20/20 - 0s - loss: 0.0749 - categorical_accuracy: 0.9761 - val_loss: 0.1530 - val_categorical_accuracy: 0.9524 - 382ms/epoch - 19ms/step
Epoch 882/1000
20/20 - 0s - loss: 0.0852 - categorical_accuracy: 0.9718 - val_loss: 0.1656 - val_categorical_accuracy: 0.9460 - 347ms/epoch - 17ms/step
Epoch 883/1000
20/20 - 0s - loss: 0.0813 - categorical_accuracy: 0.9734 - val_loss: 0.1632 - val_categorical_accuracy: 0.9492 - 362ms/epoch - 18ms/step
Epoch 884/1000
20/20 - 0s - loss: 0.0777 - categorical_accuracy: 0.9746 - val_loss: 0.1734 - val_categorical_accuracy: 0.9470 - 361ms/epoch - 18ms/step
Epoch 885/1000
20/20 - 0s - loss: 0.1071 - categorical_accuracy: 0.9626 - val_loss: 0.1699 - val_categorical_accuracy: 0.9471 - 364ms/epoch - 18ms/step
Epoch 886/1000
20/20 - 0s - loss: 0.0897 - categorical_accuracy: 0.9700 - val_loss: 0.1746 - val_categorical_accuracy: 0.9471 - 365ms/epoch - 18ms/step
Epoch 887/1000
20/20 - 0s - loss: 0.0835 - categorical_accuracy: 0.9724 - val_loss: 0.1549 - val_categorical_accuracy: 0.9526 - 356ms/epoch - 18ms/step
Epoch 888/1000
20/20 - 0s - loss: 0.0783 - categorical_accuracy: 0.9747 - val_loss: 0.1602 - val_categorical_accuracy: 0.9517 - 348ms/epoch - 17ms/step
Epoch 889/1000
20/20 - 0s - loss: 0.0782 - categorical_accuracy: 0.9745 - val_loss: 0.1786 - val_categorical_accuracy: 0.9462 - 344ms/epoch - 17ms/step
Epoch 890/1000
20/20 - 0s - loss: 0.0848 - categorical_accuracy: 0.9718 - val_loss: 0.1811 - val_categorical_accuracy: 0.9448 - 338ms/epoch - 17ms/step
Epoch 891/1000
20/20 - 0s - loss: 0.1139 - categorical_accuracy: 0.9604 - val_loss: 0.1582 - val_categorical_accuracy: 0.9521 - 354ms/epoch - 18ms/step
Epoch 892/1000
20/20 - 0s - loss: 0.0731 - categorical_accuracy: 0.9764 - val_loss: 0.1535 - val_categorical_accuracy: 0.9523 - 351ms/epoch - 18ms/step
Epoch 893/1000
20/20 - 0s - loss: 0.0815 - categorical_accuracy: 0.9733 - val_loss: 0.1916 - val_categorical_accuracy: 0.9375 - 348ms/epoch - 17ms/step
Epoch 894/1000
20/20 - 0s - loss: 0.3483 - categorical_accuracy: 0.9069 - val_loss: 0.1966 - val_categorical_accuracy: 0.9356 - 378ms/epoch - 19ms/step
Epoch 895/1000
20/20 - 0s - loss: 0.0955 - categorical_accuracy: 0.9691 - val_loss: 0.1558 - val_categorical_accuracy: 0.9509 - 368ms/epoch - 18ms/step
Epoch 896/1000
20/20 - 0s - loss: 0.0788 - categorical_accuracy: 0.9749 - val_loss: 0.1679 - val_categorical_accuracy: 0.9484 - 356ms/epoch - 18ms/step
Epoch 897/1000
20/20 - 0s - loss: 0.0845 - categorical_accuracy: 0.9716 - val_loss: 0.1603 - val_categorical_accuracy: 0.9511 - 351ms/epoch - 18ms/step
Epoch 898/1000
20/20 - 0s - loss: 0.0749 - categorical_accuracy: 0.9760 - val_loss: 0.1575 - val_categorical_accuracy: 0.9521 - 363ms/epoch - 18ms/step
Epoch 899/1000
20/20 - 0s - loss: 0.0723 - categorical_accuracy: 0.9770 - val_loss: 0.1577 - val_categorical_accuracy: 0.9522 - 360ms/epoch - 18ms/step
Epoch 900/1000
20/20 - 0s - loss: 0.0946 - categorical_accuracy: 0.9669 - val_loss: 0.3161 - val_categorical_accuracy: 0.9070 - 370ms/epoch - 19ms/step
Epoch 901/1000
20/20 - 0s - loss: 0.2389 - categorical_accuracy: 0.9297 - val_loss: 0.1518 - val_categorical_accuracy: 0.9520 - 348ms/epoch - 17ms/step
Epoch 902/1000
20/20 - 0s - loss: 0.0769 - categorical_accuracy: 0.9756 - val_loss: 0.1490 - val_categorical_accuracy: 0.9539 - 355ms/epoch - 18ms/step
Epoch 903/1000
20/20 - 0s - loss: 0.0747 - categorical_accuracy: 0.9760 - val_loss: 0.1514 - val_categorical_accuracy: 0.9533 - 356ms/epoch - 18ms/step
Epoch 904/1000
20/20 - 0s - loss: 0.0737 - categorical_accuracy: 0.9765 - val_loss: 0.1516 - val_categorical_accuracy: 0.9538 - 352ms/epoch - 18ms/step
Epoch 905/1000
20/20 - 0s - loss: 0.0728 - categorical_accuracy: 0.9767 - val_loss: 0.1662 - val_categorical_accuracy: 0.9502 - 350ms/epoch - 18ms/step
Epoch 906/1000
20/20 - 0s - loss: 0.1301 - categorical_accuracy: 0.9526 - val_loss: 0.2594 - val_categorical_accuracy: 0.9254 - 367ms/epoch - 18ms/step
Epoch 907/1000
20/20 - 0s - loss: 0.0863 - categorical_accuracy: 0.9724 - val_loss: 0.1492 - val_categorical_accuracy: 0.9542 - 349ms/epoch - 17ms/step
Epoch 908/1000
20/20 - 0s - loss: 0.0742 - categorical_accuracy: 0.9762 - val_loss: 0.1567 - val_categorical_accuracy: 0.9509 - 369ms/epoch - 18ms/step
Epoch 909/1000
20/20 - 0s - loss: 0.0729 - categorical_accuracy: 0.9766 - val_loss: 0.1595 - val_categorical_accuracy: 0.9501 - 368ms/epoch - 18ms/step
Epoch 910/1000
20/20 - 0s - loss: 0.0800 - categorical_accuracy: 0.9734 - val_loss: 0.1650 - val_categorical_accuracy: 0.9476 - 354ms/epoch - 18ms/step
Epoch 911/1000
20/20 - 0s - loss: 0.0787 - categorical_accuracy: 0.9743 - val_loss: 0.1564 - val_categorical_accuracy: 0.9526 - 363ms/epoch - 18ms/step
Epoch 912/1000
20/20 - 0s - loss: 0.0782 - categorical_accuracy: 0.9742 - val_loss: 0.1567 - val_categorical_accuracy: 0.9514 - 367ms/epoch - 18ms/step
Epoch 913/1000
20/20 - 0s - loss: 0.0786 - categorical_accuracy: 0.9743 - val_loss: 0.1715 - val_categorical_accuracy: 0.9455 - 349ms/epoch - 17ms/step
Epoch 914/1000
20/20 - 0s - loss: 0.2564 - categorical_accuracy: 0.9267 - val_loss: 0.1569 - val_categorical_accuracy: 0.9511 - 363ms/epoch - 18ms/step
Epoch 915/1000
20/20 - 0s - loss: 0.0769 - categorical_accuracy: 0.9759 - val_loss: 0.1502 - val_categorical_accuracy: 0.9540 - 352ms/epoch - 18ms/step
Epoch 916/1000
20/20 - 0s - loss: 0.0721 - categorical_accuracy: 0.9772 - val_loss: 0.1489 - val_categorical_accuracy: 0.9541 - 358ms/epoch - 18ms/step
Epoch 917/1000
20/20 - 0s - loss: 0.0712 - categorical_accuracy: 0.9777 - val_loss: 0.1577 - val_categorical_accuracy: 0.9515 - 363ms/epoch - 18ms/step
Epoch 918/1000
20/20 - 0s - loss: 0.0724 - categorical_accuracy: 0.9769 - val_loss: 0.1592 - val_categorical_accuracy: 0.9525 - 350ms/epoch - 18ms/step
Epoch 919/1000
20/20 - 0s - loss: 0.0911 - categorical_accuracy: 0.9687 - val_loss: 0.1676 - val_categorical_accuracy: 0.9496 - 348ms/epoch - 17ms/step
Epoch 920/1000
20/20 - 0s - loss: 0.0735 - categorical_accuracy: 0.9762 - val_loss: 0.1509 - val_categorical_accuracy: 0.9545 - 354ms/epoch - 18ms/step
Epoch 921/1000
20/20 - 0s - loss: 0.0721 - categorical_accuracy: 0.9768 - val_loss: 0.1579 - val_categorical_accuracy: 0.9517 - 358ms/epoch - 18ms/step
Epoch 922/1000
20/20 - 0s - loss: 0.0720 - categorical_accuracy: 0.9769 - val_loss: 0.1611 - val_categorical_accuracy: 0.9497 - 367ms/epoch - 18ms/step
Epoch 923/1000
20/20 - 0s - loss: 0.3785 - categorical_accuracy: 0.9076 - val_loss: 0.1656 - val_categorical_accuracy: 0.9468 - 338ms/epoch - 17ms/step
Epoch 924/1000
20/20 - 0s - loss: 0.0839 - categorical_accuracy: 0.9732 - val_loss: 0.1512 - val_categorical_accuracy: 0.9530 - 348ms/epoch - 17ms/step
Epoch 925/1000
20/20 - 0s - loss: 0.0748 - categorical_accuracy: 0.9762 - val_loss: 0.1497 - val_categorical_accuracy: 0.9540 - 347ms/epoch - 17ms/step
Epoch 926/1000
20/20 - 0s - loss: 0.0714 - categorical_accuracy: 0.9775 - val_loss: 0.1531 - val_categorical_accuracy: 0.9519 - 349ms/epoch - 17ms/step
Epoch 927/1000
20/20 - 0s - loss: 0.0761 - categorical_accuracy: 0.9752 - val_loss: 0.1549 - val_categorical_accuracy: 0.9517 - 354ms/epoch - 18ms/step
Epoch 928/1000
20/20 - 0s - loss: 0.0765 - categorical_accuracy: 0.9748 - val_loss: 0.1537 - val_categorical_accuracy: 0.9528 - 338ms/epoch - 17ms/step
Epoch 929/1000
20/20 - 0s - loss: 0.0873 - categorical_accuracy: 0.9705 - val_loss: 0.1518 - val_categorical_accuracy: 0.9529 - 332ms/epoch - 17ms/step
Epoch 930/1000
20/20 - 0s - loss: 0.0712 - categorical_accuracy: 0.9772 - val_loss: 0.1590 - val_categorical_accuracy: 0.9525 - 350ms/epoch - 18ms/step
Epoch 931/1000
20/20 - 0s - loss: 0.0767 - categorical_accuracy: 0.9747 - val_loss: 0.2361 - val_categorical_accuracy: 0.9316 - 349ms/epoch - 17ms/step
Epoch 932/1000
20/20 - 0s - loss: 0.2007 - categorical_accuracy: 0.9323 - val_loss: 0.1973 - val_categorical_accuracy: 0.9357 - 359ms/epoch - 18ms/step
Epoch 933/1000
20/20 - 0s - loss: 0.0809 - categorical_accuracy: 0.9740 - val_loss: 0.1623 - val_categorical_accuracy: 0.9507 - 366ms/epoch - 18ms/step
Epoch 934/1000
20/20 - 0s - loss: 0.0715 - categorical_accuracy: 0.9773 - val_loss: 0.1553 - val_categorical_accuracy: 0.9528 - 365ms/epoch - 18ms/step
Epoch 935/1000
20/20 - 0s - loss: 0.0712 - categorical_accuracy: 0.9771 - val_loss: 0.1519 - val_categorical_accuracy: 0.9539 - 358ms/epoch - 18ms/step
Epoch 936/1000
20/20 - 0s - loss: 0.0711 - categorical_accuracy: 0.9771 - val_loss: 0.1546 - val_categorical_accuracy: 0.9519 - 364ms/epoch - 18ms/step
Epoch 937/1000
20/20 - 0s - loss: 0.0801 - categorical_accuracy: 0.9735 - val_loss: 0.1615 - val_categorical_accuracy: 0.9490 - 353ms/epoch - 18ms/step
Epoch 938/1000
20/20 - 0s - loss: 0.0764 - categorical_accuracy: 0.9748 - val_loss: 0.1556 - val_categorical_accuracy: 0.9520 - 347ms/epoch - 17ms/step
Epoch 939/1000
20/20 - 0s - loss: 0.0768 - categorical_accuracy: 0.9750 - val_loss: 0.1522 - val_categorical_accuracy: 0.9528 - 368ms/epoch - 18ms/step
Epoch 940/1000
20/20 - 0s - loss: 0.0786 - categorical_accuracy: 0.9740 - val_loss: 0.1548 - val_categorical_accuracy: 0.9531 - 353ms/epoch - 18ms/step
Epoch 941/1000
20/20 - 0s - loss: 0.0706 - categorical_accuracy: 0.9774 - val_loss: 0.1541 - val_categorical_accuracy: 0.9536 - 350ms/epoch - 18ms/step
Epoch 942/1000
20/20 - 0s - loss: 0.0806 - categorical_accuracy: 0.9730 - val_loss: 0.1811 - val_categorical_accuracy: 0.9411 - 356ms/epoch - 18ms/step
Epoch 943/1000
20/20 - 0s - loss: 0.0847 - categorical_accuracy: 0.9715 - val_loss: 0.1532 - val_categorical_accuracy: 0.9533 - 352ms/epoch - 18ms/step
Epoch 944/1000
20/20 - 0s - loss: 0.0700 - categorical_accuracy: 0.9775 - val_loss: 0.1537 - val_categorical_accuracy: 0.9535 - 370ms/epoch - 19ms/step
Epoch 945/1000
20/20 - 0s - loss: 0.0689 - categorical_accuracy: 0.9777 - val_loss: 0.1561 - val_categorical_accuracy: 0.9534 - 371ms/epoch - 19ms/step
Epoch 946/1000
20/20 - 0s - loss: 0.0690 - categorical_accuracy: 0.9780 - val_loss: 0.1534 - val_categorical_accuracy: 0.9537 - 339ms/epoch - 17ms/step
Epoch 947/1000
20/20 - 0s - loss: 0.0738 - categorical_accuracy: 0.9757 - val_loss: 0.1598 - val_categorical_accuracy: 0.9525 - 369ms/epoch - 18ms/step
Epoch 948/1000
20/20 - 0s - loss: 0.0753 - categorical_accuracy: 0.9750 - val_loss: 0.1631 - val_categorical_accuracy: 0.9526 - 347ms/epoch - 17ms/step
Epoch 949/1000
20/20 - 0s - loss: 0.2822 - categorical_accuracy: 0.9256 - val_loss: 0.1672 - val_categorical_accuracy: 0.9464 - 355ms/epoch - 18ms/step
Epoch 950/1000
20/20 - 0s - loss: 0.0788 - categorical_accuracy: 0.9749 - val_loss: 0.1582 - val_categorical_accuracy: 0.9519 - 352ms/epoch - 18ms/step
Epoch 951/1000
20/20 - 0s - loss: 0.0704 - categorical_accuracy: 0.9776 - val_loss: 0.1585 - val_categorical_accuracy: 0.9524 - 350ms/epoch - 18ms/step
Epoch 952/1000
20/20 - 0s - loss: 0.0712 - categorical_accuracy: 0.9767 - val_loss: 0.1736 - val_categorical_accuracy: 0.9487 - 350ms/epoch - 18ms/step
Epoch 953/1000
20/20 - 0s - loss: 0.4668 - categorical_accuracy: 0.8797 - val_loss: 0.2140 - val_categorical_accuracy: 0.9291 - 364ms/epoch - 18ms/step
Epoch 954/1000
20/20 - 0s - loss: 0.1076 - categorical_accuracy: 0.9649 - val_loss: 0.1597 - val_categorical_accuracy: 0.9499 - 349ms/epoch - 17ms/step
Epoch 955/1000
20/20 - 0s - loss: 0.0804 - categorical_accuracy: 0.9746 - val_loss: 0.1564 - val_categorical_accuracy: 0.9514 - 353ms/epoch - 18ms/step
Epoch 956/1000
20/20 - 0s - loss: 0.0746 - categorical_accuracy: 0.9765 - val_loss: 0.1559 - val_categorical_accuracy: 0.9512 - 353ms/epoch - 18ms/step
Epoch 957/1000
20/20 - 0s - loss: 0.0725 - categorical_accuracy: 0.9770 - val_loss: 0.1556 - val_categorical_accuracy: 0.9531 - 352ms/epoch - 18ms/step
Epoch 958/1000
20/20 - 0s - loss: 0.0723 - categorical_accuracy: 0.9768 - val_loss: 0.1524 - val_categorical_accuracy: 0.9538 - 351ms/epoch - 18ms/step
Epoch 959/1000
20/20 - 0s - loss: 0.0686 - categorical_accuracy: 0.9782 - val_loss: 0.1524 - val_categorical_accuracy: 0.9537 - 351ms/epoch - 18ms/step
Epoch 960/1000
20/20 - 0s - loss: 0.0790 - categorical_accuracy: 0.9740 - val_loss: 0.1594 - val_categorical_accuracy: 0.9503 - 353ms/epoch - 18ms/step
Epoch 961/1000
20/20 - 0s - loss: 0.0774 - categorical_accuracy: 0.9746 - val_loss: 0.1522 - val_categorical_accuracy: 0.9541 - 368ms/epoch - 18ms/step
Epoch 962/1000
20/20 - 0s - loss: 0.0745 - categorical_accuracy: 0.9755 - val_loss: 0.1711 - val_categorical_accuracy: 0.9461 - 363ms/epoch - 18ms/step
Epoch 963/1000
20/20 - 0s - loss: 0.3112 - categorical_accuracy: 0.9165 - val_loss: 0.1660 - val_categorical_accuracy: 0.9474 - 355ms/epoch - 18ms/step
Epoch 964/1000
20/20 - 0s - loss: 0.0802 - categorical_accuracy: 0.9746 - val_loss: 0.1530 - val_categorical_accuracy: 0.9522 - 364ms/epoch - 18ms/step
Epoch 965/1000
20/20 - 0s - loss: 0.0713 - categorical_accuracy: 0.9778 - val_loss: 0.1495 - val_categorical_accuracy: 0.9549 - 363ms/epoch - 18ms/step
Epoch 966/1000
20/20 - 0s - loss: 0.0696 - categorical_accuracy: 0.9781 - val_loss: 0.1526 - val_categorical_accuracy: 0.9537 - 364ms/epoch - 18ms/step
Epoch 967/1000
20/20 - 0s - loss: 0.0716 - categorical_accuracy: 0.9770 - val_loss: 0.1603 - val_categorical_accuracy: 0.9510 - 353ms/epoch - 18ms/step
Epoch 968/1000
20/20 - 0s - loss: 0.0706 - categorical_accuracy: 0.9775 - val_loss: 0.1544 - val_categorical_accuracy: 0.9532 - 350ms/epoch - 18ms/step
Epoch 969/1000
20/20 - 0s - loss: 0.0685 - categorical_accuracy: 0.9781 - val_loss: 0.1551 - val_categorical_accuracy: 0.9522 - 351ms/epoch - 18ms/step
Epoch 970/1000
20/20 - 0s - loss: 0.0733 - categorical_accuracy: 0.9763 - val_loss: 0.1494 - val_categorical_accuracy: 0.9552 - 353ms/epoch - 18ms/step
Epoch 971/1000
20/20 - 0s - loss: 0.0688 - categorical_accuracy: 0.9784 - val_loss: 0.1617 - val_categorical_accuracy: 0.9512 - 337ms/epoch - 17ms/step
Epoch 972/1000
20/20 - 0s - loss: 0.0694 - categorical_accuracy: 0.9778 - val_loss: 0.1580 - val_categorical_accuracy: 0.9537 - 343ms/epoch - 17ms/step
Epoch 973/1000
20/20 - 0s - loss: 0.0686 - categorical_accuracy: 0.9780 - val_loss: 0.2028 - val_categorical_accuracy: 0.9393 - 339ms/epoch - 17ms/step
Epoch 974/1000
20/20 - 0s - loss: 0.2154 - categorical_accuracy: 0.9286 - val_loss: 0.1638 - val_categorical_accuracy: 0.9485 - 350ms/epoch - 18ms/step
Epoch 975/1000
20/20 - 0s - loss: 0.0727 - categorical_accuracy: 0.9770 - val_loss: 0.1513 - val_categorical_accuracy: 0.9533 - 351ms/epoch - 18ms/step
Epoch 976/1000
20/20 - 0s - loss: 0.0688 - categorical_accuracy: 0.9782 - val_loss: 0.1531 - val_categorical_accuracy: 0.9539 - 355ms/epoch - 18ms/step
Epoch 977/1000
20/20 - 0s - loss: 0.0696 - categorical_accuracy: 0.9776 - val_loss: 0.1540 - val_categorical_accuracy: 0.9534 - 347ms/epoch - 17ms/step
Epoch 978/1000
20/20 - 0s - loss: 0.0722 - categorical_accuracy: 0.9765 - val_loss: 0.1525 - val_categorical_accuracy: 0.9546 - 378ms/epoch - 19ms/step
Epoch 979/1000
20/20 - 0s - loss: 0.0813 - categorical_accuracy: 0.9728 - val_loss: 0.1953 - val_categorical_accuracy: 0.9359 - 367ms/epoch - 18ms/step
Epoch 980/1000
20/20 - 0s - loss: 0.2515 - categorical_accuracy: 0.9351 - val_loss: 0.1533 - val_categorical_accuracy: 0.9517 - 353ms/epoch - 18ms/step
Epoch 981/1000
20/20 - 0s - loss: 0.0724 - categorical_accuracy: 0.9769 - val_loss: 0.1519 - val_categorical_accuracy: 0.9534 - 353ms/epoch - 18ms/step
Epoch 982/1000
20/20 - 0s - loss: 0.0700 - categorical_accuracy: 0.9776 - val_loss: 0.1513 - val_categorical_accuracy: 0.9535 - 357ms/epoch - 18ms/step
Epoch 983/1000
20/20 - 0s - loss: 0.0672 - categorical_accuracy: 0.9785 - val_loss: 0.1523 - val_categorical_accuracy: 0.9547 - 359ms/epoch - 18ms/step
Epoch 984/1000
20/20 - 0s - loss: 0.0696 - categorical_accuracy: 0.9775 - val_loss: 0.1549 - val_categorical_accuracy: 0.9537 - 353ms/epoch - 18ms/step
Epoch 985/1000
20/20 - 0s - loss: 0.0782 - categorical_accuracy: 0.9745 - val_loss: 0.1509 - val_categorical_accuracy: 0.9545 - 353ms/epoch - 18ms/step
Epoch 986/1000
20/20 - 0s - loss: 0.1079 - categorical_accuracy: 0.9642 - val_loss: 0.9709 - val_categorical_accuracy: 0.8225 - 363ms/epoch - 18ms/step
Epoch 987/1000
20/20 - 0s - loss: 0.1553 - categorical_accuracy: 0.9559 - val_loss: 0.1505 - val_categorical_accuracy: 0.9540 - 359ms/epoch - 18ms/step
Epoch 988/1000
20/20 - 0s - loss: 0.0685 - categorical_accuracy: 0.9782 - val_loss: 0.1581 - val_categorical_accuracy: 0.9526 - 346ms/epoch - 17ms/step
Epoch 989/1000
20/20 - 0s - loss: 0.0673 - categorical_accuracy: 0.9789 - val_loss: 0.1565 - val_categorical_accuracy: 0.9531 - 369ms/epoch - 18ms/step
Epoch 990/1000
20/20 - 0s - loss: 0.0711 - categorical_accuracy: 0.9770 - val_loss: 0.1666 - val_categorical_accuracy: 0.9512 - 363ms/epoch - 18ms/step
Epoch 991/1000
20/20 - 0s - loss: 0.0721 - categorical_accuracy: 0.9763 - val_loss: 0.1634 - val_categorical_accuracy: 0.9510 - 354ms/epoch - 18ms/step
Epoch 992/1000
20/20 - 0s - loss: 0.0688 - categorical_accuracy: 0.9776 - val_loss: 0.1526 - val_categorical_accuracy: 0.9546 - 363ms/epoch - 18ms/step
Epoch 993/1000
20/20 - 0s - loss: 0.0668 - categorical_accuracy: 0.9788 - val_loss: 0.1538 - val_categorical_accuracy: 0.9541 - 358ms/epoch - 18ms/step
Epoch 994/1000
20/20 - 0s - loss: 0.0711 - categorical_accuracy: 0.9768 - val_loss: 0.1690 - val_categorical_accuracy: 0.9467 - 353ms/epoch - 18ms/step
Epoch 995/1000
20/20 - 0s - loss: 0.2164 - categorical_accuracy: 0.9355 - val_loss: 0.1560 - val_categorical_accuracy: 0.9526 - 369ms/epoch - 18ms/step
Epoch 996/1000
20/20 - 0s - loss: 0.0692 - categorical_accuracy: 0.9781 - val_loss: 0.1539 - val_categorical_accuracy: 0.9542 - 349ms/epoch - 17ms/step
Epoch 997/1000
20/20 - 0s - loss: 0.0687 - categorical_accuracy: 0.9780 - val_loss: 0.1547 - val_categorical_accuracy: 0.9535 - 351ms/epoch - 18ms/step
Epoch 998/1000
20/20 - 0s - loss: 0.0676 - categorical_accuracy: 0.9783 - val_loss: 0.1515 - val_categorical_accuracy: 0.9543 - 348ms/epoch - 17ms/step
Epoch 999/1000
20/20 - 0s - loss: 0.0734 - categorical_accuracy: 0.9759 - val_loss: 0.1549 - val_categorical_accuracy: 0.9531 - 354ms/epoch - 18ms/step
Epoch 1000/1000
20/20 - 0s - loss: 0.0721 - categorical_accuracy: 0.9768 - val_loss: 0.1631 - val_categorical_accuracy: 0.9487 - 344ms/epoch - 17ms/step

#reticulate::py_last_error()

#We can then compute the average of the per-epoch ACC scores for all folds:

average_acc_history <- data.frame(
  epoch = seq(1:ncol(all_acc_histories)),
  validation_acc = apply(all_acc_histories, 2, mean)
)


head(max(average_acc_history$validation_acc))

[1] 0.9543677

library(ggplot2)
ggplot(average_acc_history, aes(x = epoch, y = validation_acc)) + geom_line()

#It may be a bit hard to see the plot due to scaling issues and relatively high variance. Let's use `geom_smooth()` to try to get a clearer picture:
ggplot(average_acc_history, aes(x = epoch, y = validation_acc)) + geom_smooth()

# Evaluate on Testset
eval <- evaluate(model, test_data, test_targets, verbose = 1)

  1/423 [..............................] - ETA: 18s - loss: 0.6146 - categorical_accuracy: 0.8750
  7/423 [..............................] - ETA: 3s - loss: 0.9975 - categorical_accuracy: 0.8438 
 17/423 [>.............................] - ETA: 2s - loss: 1.2229 - categorical_accuracy: 0.7868
 29/423 [=>............................] - ETA: 2s - loss: 1.2971 - categorical_accuracy: 0.7619
 40/423 [=>............................] - ETA: 2s - loss: 1.2812 - categorical_accuracy: 0.7672
 53/423 [==>...........................] - ETA: 1s - loss: 1.1656 - categorical_accuracy: 0.7818
 65/423 [===>..........................] - ETA: 1s - loss: 1.1753 - categorical_accuracy: 0.7760
 80/423 [====>.........................] - ETA: 1s - loss: 1.2079 - categorical_accuracy: 0.7766
 92/423 [=====>........................] - ETA: 1s - loss: 1.2349 - categorical_accuracy: 0.7724
105/423 [======>.......................] - ETA: 1s - loss: 1.2360 - categorical_accuracy: 0.7688
117/423 [=======>......................] - ETA: 1s - loss: 1.2952 - categorical_accuracy: 0.7626
131/423 [========>.....................] - ETA: 1s - loss: 1.3186 - categorical_accuracy: 0.7624
145/423 [=========>....................] - ETA: 1s - loss: 1.3324 - categorical_accuracy: 0.7593
159/423 [==========>...................] - ETA: 1s - loss: 1.3150 - categorical_accuracy: 0.7616
171/423 [===========>..................] - ETA: 1s - loss: 1.3196 - categorical_accuracy: 0.7635
178/423 [===========>..................] - ETA: 1s - loss: 1.3249 - categorical_accuracy: 0.7632
182/423 [===========>..................] - ETA: 1s - loss: 1.3129 - categorical_accuracy: 0.7644
190/423 [============>.................] - ETA: 1s - loss: 1.3066 - categorical_accuracy: 0.7663
197/423 [============>.................] - ETA: 1s - loss: 1.3132 - categorical_accuracy: 0.7659
208/423 [=============>................] - ETA: 1s - loss: 1.3202 - categorical_accuracy: 0.7656
226/423 [===============>..............] - ETA: 0s - loss: 1.3268 - categorical_accuracy: 0.7624
244/423 [================>.............] - ETA: 0s - loss: 1.3475 - categorical_accuracy: 0.7600
263/423 [=================>............] - ETA: 0s - loss: 1.3410 - categorical_accuracy: 0.7611
280/423 [==================>...........] - ETA: 0s - loss: 1.3284 - categorical_accuracy: 0.7627
298/423 [====================>.........] - ETA: 0s - loss: 1.3447 - categorical_accuracy: 0.7629
316/423 [=====================>........] - ETA: 0s - loss: 1.3351 - categorical_accuracy: 0.7636
332/423 [======================>.......] - ETA: 0s - loss: 1.3276 - categorical_accuracy: 0.7634
351/423 [=======================>......] - ETA: 0s - loss: 1.3333 - categorical_accuracy: 0.7623
370/423 [=========================>....] - ETA: 0s - loss: 1.3312 - categorical_accuracy: 0.7619
389/423 [==========================>...] - ETA: 0s - loss: 1.3304 - categorical_accuracy: 0.7623
407/423 [===========================>..] - ETA: 0s - loss: 1.3311 - categorical_accuracy: 0.7628
423/423 [==============================] - 2s 4ms/step - loss: 1.3329 - categorical_accuracy: 0.7621

423/423 [==============================] - 2s 4ms/step - loss: 1.3329 - categorical_accuracy: 0.7621

head(eval)

                loss categorical_accuracy 
            1.332938             0.762109 

# # Save model and history, please change the name
#  write.csv(average_acc_history, "../Doc/Versuch 11 - 6 Layer - 1 256 - Smote/Try 11.csv", row.names=FALSE)
#  save_model_hdf5(model, "../Doc/Versuch 11 - 6 Layer - 1 256 - Smote/model 11.hfd5", overwrite = TRUE, include_optimizer = TRUE)
# 
# # Save Training, Testing and Validation Data
#  write.csv(train_data, "../Doc/Versuch 11 - 6 Layer - 1 256 - Smote/train_data.csv", row.names=FALSE)
#  write.csv(test_data, "../Doc/Versuch 11 - 6 Layer - 1 256 - Smote/test_data.csv", row.names=FALSE)
#  write.csv(train_targets, "../Doc/Versuch 11 - 6 Layer - 1 256 - Smote/train_targets.csv", row.names=FALSE)
#  write.csv(test_targets, "../Doc/Versuch 11 - 6 Layer - 1 256 - Smote/test_targets.csv", row.names=FALSE)


# Load model
# Use model_history as precaution
# model_history <- load_model_hdf5("../Doc/Versuch 6/model 6.hfd5", custom_objects = NULL, compile = TRUE)